cognitive psychology experiments on attention

Theories of Selective Attention in Psychology

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

An endless array of internal and external stimuli, thoughts, and emotions constantly bombards us. Given this abundance of available data, it is amazing that we make sense of anything!

In varying degrees of efficiency, we have developed the ability to focus on what is important while blocking out the rest.

What is Selective Attention?

Selective attention is the process of directing our awareness to relevant stimuli while ignoring irrelevant stimuli in the environment.

This is an important process as there is a limit to how much information can be processed at a given time, and selective attention allows us to tune out insignificant details and focus on what is important.

This limited capacity for paying attention has been conceptualized as a bottleneck, which restricts the flow of information.  The narrower the bottleneck, the lower the rate of flow.

Broadbent’s and Treisman’s Models of Attention are all bottleneck models because they predict we cannot consciously attend to all of our sensory input at the same time.

Broadbent’s Filter Model

Broadbent’s Attentional Theory, also known as the Filter Theory of Attention, proposes that humans can only process a limited amount of sensory information at any given time due to an attentional “bottleneck.”

Broadbent (1958) proposed that the physical characteristics of messages are used to select one message for further processing and that all others are lost.

Information from all stimuli presented at any time enters an unlimited-capacity sensory buffer.

One of the inputs is then selected based on its physical characteristics (such as pitch or loudness) for further processing by being allowed to pass through a filter.

Because we have only a limited capacity to process information, this filter is designed to prevent the information-processing system from becoming overloaded.

The inputs not initially selected by the filter remain briefly in the sensory buffer store, and if they are not processed, they decay rapidly.  Broadbent assumed that the filter rejected the unattended message at an early processing stage.

According to Broadbent, the meaning of any of the messages is not taken into account at all by the filter.  All semantic processing is carried out after the filter has selected the message to pay attention to. So whichever message(s) are restricted by the bottleneck (i.e., not selective) is not understood.

Broadbent wanted to see how people could focus their attention (selectively attend), and to do this; he deliberately overloaded them with stimuli.

One of the ways Broadbent achieved this was by simultaneously sending one message to a person’s right ear and a different message to their left ear.

This is called a split-span experiment (the dichotic listening task).

Dichotic Listening Task

The dichotic listening tasks involves simultaneously sending one message (a 3-digit number) to a person’s right ear and a different message (a different 3-digit number) to their left ear.

Participants were asked to listen to both messages simultaneously and repeat what they heard.  This is known as a “dichotic listening task.”

Broadbent was interested in how these would be repeated back. Would the participant repeat the digits back in the order that they were heard (order of presentation), or repeat back what was heard in one ear followed by the other ear (ear-by-ear).

He found that people made fewer mistakes repeating back ear by ear and would usually repeat back this way.

Evaluation of Broadbent’s Model

1. Broadbent’s dichotic listening experiments have been criticized because:

• The early studies all used people who were unfamiliar with shadowing and so found it very difficult and demanding.  Eysenck and Keane (1990) claim that the inability of naive participants to shadow successfully is due to their unfamiliarity with the shadowing task rather than an inability of the attentional system.
• Participants reported after the entire message had been played – it is possible that the unattended message is analyzed thoroughly, but participants forget.
• Analysis of the unattended message might occur below the level of conscious awareness.  For example, research by Von Wright et al. (1975) indicated analysis of the unattended message in a shadowing task.  A word was first presented to participants with a mild electric shock.  When the same word was later presented to the unattended channel, participants registered an increase in GSR (indicative of emotional arousal and analysis of the word in the unattended channel).
• More recent research has indicated the above points are important: e.g., Moray (1959) studied the effects of the practice.  Naive subjects could only detect 8% of digits appearing in either the shadowed or non-shadowed message; Moray (an experienced “shadower”) detected 67%.

2. Broadbent’s theory predicts that hearing your name when you are not paying attention should be impossible because unattended messages are filtered out before you process the meaning – thus, the model cannot account for the “Cocktail Party Phenomenon.”

3 . Other researchers have demonstrated the “ cocktail party effect ” (Cherry, 1953) under experimental conditions and have discovered occasions when information heard in the unattended ear “broke through” to interfere with information participants are paying attention to in the other ear.

This implies some analysis of the meaning of stimuli must have occurred prior to the selection of channels.  In Broadbent’s model, the filter is based solely on sensory analysis of the physical characteristics of the stimuli.

Treisman’s Attenuation Model

Treisman (1964) agrees with Broadbent’s theory of an early bottleneck filter. However, the difference is that Treisman’s filter attenuates rather than eliminates the unattended material.

Attenuation is like turning down the volume so that if you have four sources of sound in one room (TV, radio, people talking, baby crying), you can turn down or attenuate 3 to attend to the fourth.

This means people can still process the meaning of the attended message(s).

In her experiments, Treisman demonstrated that participants could still identify the contents of an unattended message, indicating that they were able to process the meaning of both the attended and unattended messages.

Treisman carried out dichotic listening tasks using the speech shadowing method.  Typically, in this method, participants are asked to simultaneously repeat aloud speech played into one ear (called the attended ear) while another message is spoken to the other ear.

For example, participants were asked to shadow “I saw the girl furniture over” and ignore “me that bird green jumping fee,” reported hearing “I saw the girl jumping over.”

Clearly, then, the unattended message was being processed for meaning, and Broadbent’s Filter Model, where the filter was extracted based on physical characteristics only, could not explain these findings.  The evidence suggests that Broadbent’s Filter Model is inadequate and does not allow for meaning to be taken into account.

Evaluation of Treisman’s Model

1. Treisman’s Model overcomes some of the problems associated with Broadbent’s Filter Model, e.g., the Attenuation Model can account for the “Cocktail Party Syndrome.”

2. Treisman’s model does not explain how exactly semantic analysis works.

3. The nature of the attenuation process has never been precisely specified.

4. A problem with all dichotic listening experiments is that you can never be sure that the participants have not actually switched attention to the so-called unattended channel.

Broadbent, D. (1958). Perception and Communication. London: Pergamon Press.

Cherry, E. C. (1953). Some experiments on the recognition of speech with one and with two ears. Journal of the Acoustical Society of America , 25, 975–979.

Eysenck, M. W. & Keane, M. T. (1990). Cognitive psychology: a student’s handbook . Hove: Lawrence Erlbaum Associates Ltd.

Moray, N. P. (1959). Attention in dichotic listening: Affective cues and the influence of instructions. Quarterly Journal of Experimental Psychology , 11, 56–60.

Treisman, A., 1964. Selective attention in man. British Medical Bulletin , 20, 12-16.

Von Wright, J. M., Anderson, K., & Stenman, U. (1975). Generalization of conditioned GSRs in dichotic listening. In P. M. A. Rabbitt & S. Dornic (Eds.), Attention and performance (Vol. V, pp. 194–204). London: Academic Press.

Keep Learning

How We Use Selective Attention to Filter Information and Focus

BBC Radio: Donald Broadbent and the Cocktail Party.

Attention Journal Article

Attention Essay

• Original article
• Open access
• Published: 22 July 2021

How do we measure attention? Using factor analysis to establish construct validity of neuropsychological tests

• Melissa Treviño   ORCID: orcid.org/0000-0002-7713-8193 1 ,
• Xiaoshu Zhu 2 ,
• Yi Yi Lu 3 , 4 ,
• Luke S. Scheuer 3 , 4 ,
• Eliza Passell 3 , 4 ,
• Grace C. Huang 2 ,
• Laura T. Germine 3 , 4 &
• Todd S. Horowitz 1  

Cognitive Research: Principles and Implications volume  6 , Article number:  51 ( 2021 ) Cite this article

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We investigated whether standardized neuropsychological tests and experimental cognitive paradigms measure the same cognitive faculties. Specifically, do neuropsychological tests commonly used to assess attention measure the same construct as attention paradigms used in cognitive psychology and neuroscience? We built on the “general attention factor”, comprising several widely used experimental paradigms (Huang et al., 2012). Participants ( n  = 636) completed an on-line battery (TestMyBrain.org) of six experimental tests [Multiple Object Tracking, Flanker Interference, Visual Working Memory, Approximate Number Sense, Spatial Configuration Visual Search, and Gradual Onset Continuous Performance Task (Grad CPT)] and eight neuropsychological tests [Trail Making Test versions A & B (TMT-A, TMT-B), Digit Symbol Coding, Forward and Backward Digit Span, Letter Cancellation, Spatial Span, and Arithmetic]. Exploratory factor analysis in a subset of 357 participants identified a five-factor structure: (1) attentional capacity (Multiple Object Tracking, Visual Working Memory, Digit Symbol Coding, Spatial Span), (2) search (Visual Search, TMT-A, TMT-B, Letter Cancellation); (3) Digit Span; (4) Arithmetic; and (5) Sustained Attention (GradCPT). Confirmatory analysis in 279 held-out participants showed that this model fit better than competing models. A hierarchical model where a general cognitive factor was imposed above the five specific factors fit as well as the model without the general factor. We conclude that Digit Span and Arithmetic tests should not be classified as attention tests. Digit Symbol Coding and Spatial Span tap attentional capacity, while TMT-A, TMT-B, and Letter Cancellation tap search (or attention-shifting) ability. These five tests can be classified as attention tests.

Significance statement

Assessment of cognitive function in clinical populations, for both clinical and research purposes, is primarily based on standardized neuropsychological testing. However, this approach is limited as a clinical research tool due to two major issues: sensitivity and construct validity. Deriving new measures based on contemporary work in cognitive psychology and cognitive neuroscience could help to solve these problems. However, we do not understand the relationship between existing neuropsychological tests and widely used cognitive paradigms. The goal of this paper is to begin to address this problem, using factor analysis tools to map the relationships, specifically in the domain of attention. Our results should provide guidance for which neuropsychological tests should be classified as attention tests, and hopefully provide inspiration for the development of new clinical assessments based on experimental attention paradigms. Furthermore, we hope we have provided a template for other researchers to explore the connections between cognitive paradigms and neuropsychological tests in domains beyond attention. By bringing these fields closer together, we can improve our scientific understanding of cognition, and ultimately improve the welfare of people who suffer from cognitive disorders and deficits.

Introduction

Assessing cognitive functioning across the gamut of health and mental health conditions has traditionally relied on standardized neuropsychological test batteries (Foti et al., 2017 ; Helmstaedter et al., 2003 ; Meade et al., 2018 ; Vives et al., 2015 ). However, this approach may be reaching its limits as a clinical research tool in many fields, due to two major issues: sensitivity and construct validity (Bilder & Reise, 2019 ; Horowitz et al., 2018 ; Howieson, 2019 ; Kessels, 2019 ; Marcopulos & Łojek, 2019 ; Parsons & Duffield, 2019 ). We and others have proposed that deriving new measures based on contemporary work in cognitive psychology and cognitive neuroscience could help to solve these problems (Carter & Barch, 2007 ; Horowitz et al., 2018 ). However, we currently do not understand the relationship between existing neuropsychological tests and widely used cognitive paradigms. The goal of this paper is to begin to address this problem, using factor analysis tools to map the relationships. Specifically, we will address the attention domain, which was the most frequently assessed cognitive domain in our survey of cancer-related cognitive impairment studies (Horowitz et al., 2019 ).

Many neuropsychological tests were originally designed to diagnose severe cognitive difficulties (e.g., resulting from stroke). As a result, they tend to lack sensitivity to the less severe, and often more diffuse cognitive difficulties encountered by many clinical populations (Nelson & Suls, 2013 ). This insensitivity may contribute to the widely observed lack of correlation between objective neuropsychological tests and patients’ subjective reports of their own cognitive problems (Jenkins et al., 2006 ; Srisurapanont et al., 2017 ).

Neuropsychological tests tend to be developed from a practical rather than a theoretical standpoint, and often tap multiple cognitive abilities in a single test (Sohlberg & Mateer, 1989 ). This means that it is often difficult to know exactly what cognitive faculties are being measured by a given test (Kessels, 2019 ; Schmidt et al., 1994 ). The Digit Symbol Coding test, for example, is a widely used neuropsychological test that is variously held to measure attention, psychomotor speed, working memory, processing speed, and executive function (Horowitz et al., 2019 ). In the clinical setting, this lack of specificity can be an advantage. If a patient has cognitive problems, they are likely to show up on the Digit Symbol Coding test. However, the downside is that it is very difficult to pin down which cognitive faculties are affected (Jaeger, 2018 ). For research purposes, this construct validity problem is a major limitation (McFall, 2005 ; McFall & Townsend, 1998 ), and poses a general challenge to integrating neuropsychological research with cognitive neuroscience (Horowitz et al., 2019 ).

In contrast to the neuropsychological tradition, experimental paradigms (“paradigms” rather than “tests”, because there is no standard version; Kessels, 2019 ) in basic cognitive psychology and cognitive neuroscience are explicitly created to test theoretical models of specific cognitive functions and operations. Experimental paradigms often have internal manipulations that allow for separations of subcomponent processes. Consider the visual search paradigm, in which observers search through N items to find a target (e.g., search for the T among Ls). Instead of looking at the overall response time, the experimenter computes the slope of the regression line for response time as a function of N to yield a pure search rate, independent of perceptual, response, and motor stages (Sternberg, 1966 ). Similarly, in the Eriksen flanker paradigm (Eriksen & Eriksen, 1974 ) participants see three letters, and are asked to give one response if the central letter belongs to one category (e.g., X or T) and another response if it belongs to another (e.g., O or C). If the two flanking letters come from the same category as the target (e.g., X T X, compatible trial), responses are typically faster than when they come from different categories (e.g., X C X, incompatible). The primary dependent measure is again not overall response time, but the incompatible-compatible difference score, which provides a measure of the strength of interference from incompatible responses. This sort of logic is rare in neuropsychological tests, perhaps in part because they have been largely administered, even now, as paper-and-pencil tests. Consider the Trail Making Test (Partington & Leiter, 1949 ), in which participants are asked to connect a sequence of digits in order (1, 2, 3, etc., version A) or alternate between digits and letters (1, A, 2, B, etc., version B). The score for each version is overall completion time. This score conflates search ability, motor function, and executive function into a single score. As with the Digit Symbol Coding test, this makes the Trail Making Test sensitive to a wide range of deficits, which contributes to its popularity (along with the fact that it is not proprietary), while making the results difficult to interpret (Kessels, 2019 ) though several groups have attempted to decompose Trail Making Test performance (Crowe, 1998 ; Misdraji & Gass, 2010 ; Salthouse, 2011 ). Interestingly, the Trail Making Test does produce a difference score: the difference between versions A and B is held to measure target-switching or executive function (Crowe, 1998 ; Sánchez-Cubillo, 2009 ). However, this difference score is rarely reported in the scientific literature (e.g., in the cancer-related cognitive impairment field, see Horowitz et al., 2019 ).

One way to bridge this gap is to take experimental paradigms and adapt them to the demands of clinical research and practice. Several recent ventures in this direction have been based on Posner and Petersen’s ( 1990 ) influential Attentional Network theory, which proposed that attention is divided into three separate functional brain networks. The alerting network is defined as maintaining a vigilant and alert state, the orienting network involves directing attention in space, and the executive control is responsible for resolving conflict between responses (MacLeod et al., 2010 ). Based on this conceptualization of attention, Fan et al. ( 2002 ) developed the Attentional Network Test, which combines a flanker task (with incongruent, congruent, and neutral trials) and a cued reaction time task (with central, double, spatial, and no cue trials). The difference between trials with congruent and incongruent flankers measures executive control, the difference between central and spatial cues measures orienting, and the difference between double and no-cue trials measures orienting. The Dalhousie Computerized Attention Battery (Jones et al., 2016 ) was also designed to measure the alerting, orienting, and executive control networks, using a battery of eight tasks adapted from the experimental literature. Simple and choice reaction time tasks measure alerting, visual search measures orienting, while a go/no go task, a dual task, a flanker task, and item and location working memory tasks measure executive control. The NIH Toolbox ( https://www.healthmeasures.net/explore-measurement-systems/nih-toolbox ) is a comprehensive set of computerized tests designed to measure cognitive, emotional, sensory, and motor functions. The NIH Toolbox Cognition Battery, part of the NIH Toolbox initiative, uses a version of the flanker test derived from the ANT to measure inhibitory control (Zelazo et al., 2013 ).

The ANT has been adopted fairly widely. A review by Arora, Lawrence, and Klein (Arora et al., 2020 ) found 889 studies using the ANT through 2019. Similarly, the paper describing the NIH Toolbox Cognition Battery Executive Function and Attention components has been cited in 244 papers as of early 2021. The Dalhousie battery has not had much time to gain traction; we could only find two non-methodological papers that had cited it (Cunningham et al., 2018 ; Sardiwalla et al., 2019 ). However, none of the three batteries showed up in our survey of meta-analyses of cancer-related cognitive impairment (Horowitz et al., 2019 ), or in a survey of practicing clinical neuropsychologists (Rabin et al., 2016 ).

We propose that factor analysis can serve as a useful tool to help establish construct validity. By mapping the relationships between neuropsychological tests and experimental paradigms, we can gain a better understanding of how neuropsychological tests relate to contemporary theories in cognitive psychology and cognitive neuroscience. Our approach is to run a set of participants on a battery composed of experimental cognitive paradigms from the attention literature and neuropsychological tests commonly used to measure attention in clinical populations, and use factor analysis to see whether the neuropsychological tests load on the same factor as the experimental paradigms.

Factor analysis is widely used in the development and validation of neuropsychological test batteries, but typically this is only done to understand the factor structure within a battery (e.g., Jones et al., 2015 ; Price et al., 2002 ). Two studies have used factor analysis to understand the relationships among neuropsychological tests commonly used to assess attention. Mirsky et al. ( 1991 ) found that many tests loaded onto a “perceptual-motor speed” factor (including Stroop, the Trail Making Test, Digit Symbol Coding, and Digit Cancellation), a “numerical-mnemonic” factor (Digit Span and Arithmetic) a “vigilance” factor (Continuous Performance Test) and a “flexibility” factor (Wisconsin Card Sorting Test). Schmidt et al. ( 1994 ) found separate factors for “scanning” (Stroop, TMT-A & -B, Digit Symbol Coding) and “spanning” (Visual Span, Digit Span Forwards, Digit Span Backwards).

In studies of intelligence, the Cattell–Horn–Carroll taxonomy (McGrew, 2009 ) is an influential factor-analytic scheme for organizing tests into domains (termed “broad abilities). While the taxonomy was developed on intelligence batteries, there is a certain amount of overlap with neuropsychological testing, where batteries (or tests from batteries) such as the Wechsler Adult Intelligence Scale often serve clinical purposes. Recent meta-factor-analytic work from Jewsbury et al. ( 2017 ) and Agelink van Rentergem et al. ( 2020 ) show that the Cattell–Horn–Carroll framework also fits well to data from neuropsychological batteries.

Studies that have tried to map clinical neuropsychological tests to laboratory or experimental paradigms are rare. In the working memory domain, Shelton et al. ( 2009 ) showed that clinical tests from the Wechsler batteries (Wechsler Adult Intelligence Scale III and Wechsler Memory Scale) correlated poorly with a factor defined by laboratory paradigms such as operation span (OSPAN, Turner & Engle, 1989 ), while the laboratory paradigms better predicted fluid intelligence. To our knowledge, no one has used factor analysis to study the relationship between neuropsychological attention tests and experimental attention paradigms.

As an object of study, attention lacks the sort of strong criterion construct that fluid intelligence presents for working memory (Shelton et al., 2009 ). However, a study from Huang et al. ( 2012 ) provides a useful starting point. Huang et al. tested 257 participants on 17 different experimental paradigms drawn from the attention literature, including nine “primary” and eight “secondary” paradigms, selected for their theoretical relevance as attention measures (Bundesen, 1990 ; Desimone & Duncan, 1995 ; Huang & Pashler, 2007 ; Posner & Petersen, 1990 ; Treisman & Gelade, 1980 ). In a principal components analysis, all nine primary paradigms loaded strongly on a single general attention factor, which Huang et al. termed a , with analogy to g , the general intelligence factor. The a factor accounted for 35% of total task variance, while 65% of the variance was explained by task-specific mechanisms. This result suggests that there is a single underlying attention mechanism that can be measured with one of the nine primary paradigms. To be precise, a should be regarded as a selective attention factor, as there were no sustained attention paradigms in Huang et al.’s battery.

In contrast, a study from Skogsberg et al. ( 2015 ), who tested 222 participants on 11 tasks, concluded that attention measures could be meaningfully divided into four clusters: spatiotemporal attention, global attention, transient attention, and sustained attention. Unfortunately, the set of attention tests analyzed by Skogsberg et al. has very little overlap with the Huang et al. ( 2012 ) set; only Multiple Object Tracking was included in both analyses. It is possible that Huang et al.’s a is a subset of the four clusters identified by Skogsberg et al. We return to this topic in the discussion.

The plan for the current study was to determine whether neuropsychological tasks used to measure attention would load on to the same factor(s) as tests derived from experimental cognitive paradigms. We chose to design our study around Huang et al. ( 2012 )’s a factor. By using a single factor that would correlate with multiple tests, we thought to achieve more power while simplifying the interpretation of the data.

We designed a battery composed of six experimental tests, and eight neuropsychological tests. The experimental tests included five selective attention paradigms, and one sustained attention test. The five selective attention tests (multiple object tracking, spatial configuration visual search, visual working memory, approximate number sense, and flanker interference) were selected to represent the paradigms that had the strongest correlations (i.e., 0.56–0.67) to the a factor in Huang et al.’s ( 2012 ) study, were widely used in experimental, cognitive psychology, and had strong theoretical justifications as attention measures. It is important to note that our tests were not identical to those used by Huang et al. This project was not an attempt to replicate Huang et al., but to build on the concept of the general attention factor. In some cases (multiple object tracking, visual working memory, approximate number sense, and flanker interference), we opted to use tasks that were already available on the TestMyBrain platform.

The multiple object tracking (MOT) task corresponds to the Tracking paradigm in Huang et al.’s ( 2012 ) battery. In MOT, participants need to remember and track a set of targets among a larger set of identical distractors. This requires selective attention to track targets while excluding distractors. Selective attention is central to successful performance in these tasks (Holcombe et al., 2014 ; Vul et al., 2009 ), and the paradigm has been a useful proving ground for models of attention (Cavanagh & Alvarez, 2005 ; Pylyshyn & Storm, 1988 ; Scholl, 2009 ). A version of the MOT was included in the Skogsberg et al. ( 2015 ) battery. Our MOT task asked participants to track 3–5 out of 10 disks, while Huang et al.’s Tracking paradigm had participants track 4 out of 8 disks.

Visual search has played a central role in attentional theory for decades (Schneider & Shiffrin, 1977 ; Treisman & Gelade, 1980 ). Spatial configuration search, where targets are distinguished from distractors only by the internal arrangement of components, is widely held to index serial shifts of covert attention (Bricolo et al., 2002 ; Wolfe, 2021 ; Woodman & Luck, 1999 ). Here we employed the widely used spatial configuration search for T-shaped targets among L-shaped distractors. This corresponds to Huang et al.’s ( 2012 ) Configuration Search task for squares that were white-above-black among black-above-white.

Visual working memory (VWM) may seem like an odd choice to measure attention, especially when we are trying to distinguish between attention and working memory functions. However, VWM is a distinct, modality-specific memory store (Fougnie & Marois, 2011 ), and is tightly linked to selective attention, in that both encoding (Emrich et al., 2017 ; Praß & de Haan, 2019 ) and maintenance in VWM require visual attention (Makovski et al., 2008 ; Roper & Vecera, 2014 ; Sandry & Ricker, 2020 ). Huang et al. ( 2012 ) used a Visual Short-Term Memory task requiring participants to memorize an array of six colors and then recreate this array from memory after it offset (i.e., full report paradigm). However, it is much more common to use a change-detection paradigm to measure visual short-term or working memory capacity (Luck & Vogel, 1997 ; Pashler, 1988 ). In a change detection paradigm, the whole array is presented at recall, and the participant has to indicate whether or not any element has changed. This approach is more time-efficient, and also avoids the complication of changes in the state of the memory during an extended report process (Peters et al., 2018 ). Accordingly, we measured Visual Working Memory (VWM) in a change-detection paradigm where participants had to memorize four shapes and report whether one of them changed after a brief delay.

Enumeration and numerosity perception are also tightly linked to selective attention. Specifically, enumeration can be described as an attentional individuation mechanism (Mazza & Caramazza, 2015 ). Whether we perceive precise counts or estimates depends on whether attention is focused or distributed (Chong & Evans, 2011 ). To measure numerosity perception, Huang et al. ( 2012 ) employed a Counting task that required participants to report whether the number of dots on the screen was even or odd. We opted for the Approximate Number Sense (ANS, Halberda et al., 2012 ) task that required participants to indicate whether there were more blue dots than yellow dots or vice versa. The ANS task is more strongly linked to attention, since it is a selective enumeration task, requiring participants to filter out irrelevant items.

The final paradigm was response selection, a form of internal attention (Chun et al., 2011 ) involving selection between competing actions. Huang et al. ( 2012 )’s Response Selection task was a 4-alternative forced-choice response to the color of a ball. We chose the Flanker Interference task (Eriksen & Eriksen, 1974 ), which requires participants to respond to a central target in the presence of irrelevant flanking stimuli that could be congruent or incongruent with the correct response. This choice was made partly for theoretical reasons, in that the requirement to filter out distraction makes the Flanker Interference task more of a selective attention task than a forced-choice response time task. Additionally, the Flanker Interference task is more widely used, both in experimental cognitive psychology and in neuropsychology. The Attentional Network Task (Fan et al., 2002 ), the Dalhousie Computerized Attention Battery, and the NIH Toolbox Cognition Battery executive function and attention sub-battery (Zelazo et al., 2013 ) all include a Flanker Interference component.

Finally, we also included a sustained attention test, the Gradual Onset Continuous Performance Test (gradCPT). The gradCPT is similar to continuous performance tasks that require frequent responses, such as the Sustained Attention to Response Task (Robertson et al., 1997 ), except that the gradCPT uses gradual transitions between stimuli, rather than abrupt onsets that may capture attention (Yantis & Jonides, 1990 ) and thus reduce sensitivity to vigilance decrements (Rosenberg et al., 2013 ). The gradCPT has been demonstrated to be sensitive to individual differences (Fortenbaugh et al., 2015 ; Rosenberg et al., 2013 ).

In contrast, the eight neuropsychological tests (Trail Making Test versions A & B (TMT-A, TMT-B), Digit Symbol Coding, Forward and Backward Digit Span, Letter Cancellation, Spatial Span, and Arithmetic) were not chosen for their theoretical or empirical links to attention. We selected the tests most frequently used to measure attention in our review of the literature on cancer-related cognitive impairment in cancer survivors (Horowitz et al., 2019 ). Digit span, arithmetic, letter cancellation, and the Trail Making Test were also among the most frequently used tests for “attention, concentration, and working memory” in a survey of the membership lists of the National Academy of Neuropsychology and the International Neuropsychological Society (Rabin et al., 2016 ), so we believe that this usage is typical of neuropsychological practice.

Historically, the neuropsychological tests used to measure attention have not been grounded in attentional theory (Mirsky et al., 1991 ; Schmidt et al., 1994 ). Tests such as Digit Span (measuring the number of digits participants can recall) and Arithmetic (ability to solve mathematical word problems) would seem to have little relationship to attention, since they do not require any sort of selection or filtering. Indeed, in our database of cancer-related cognitive impairment studies (Horowitz et al., 2019 ), these tests are also frequently classified under working memory, rather than attention. Then again, given that visual working memory and numerosity perception tests do seem to be linked to attention, we should not rule out these tests as attention measures out of hand. The Trail Making and Letter Cancellation tests closely resemble the visual search paradigm. However, as noted above, it is difficult to parse out the search component from motor factors or ability to alternate sequences (in the TMT-B). The Digit Symbol Coding test, in which participants are given a symbol-number key and asked to match a presented symbol to its corresponding number within an allowed time, similarly seems to tap into a number of cognitive domains, including visual search. Spatial Span, a visuospatial analog of the Digit Span tests, may be related to multitarget spatial attention tasks such as MOT (Trick et al., 2012 ). Notably, the Spatial Span test is alone among the neuropsychology tests we used in that it is never classified as anything other than an attention test in our dataset (Horowitz et al., 2019 ).

We hypothesized that the five selective attention paradigms would load on a common factor, a . We included the sustained attention paradigm on a hunch that some of the neuropsychological tests were better at predicting vigilance than selection. The key research question was which of the neuropsychological tests, if any, would also load on the a factor.

Participants

Sample size.

Recommendations for determining the minimal sample size for factor analysis have been diverse and often contradictory (MacCallum et al., 1999 ; Preacher & MacCallum, 2002 ), though Thompson ( 2004 ) suggested that a sample size of 300 is generally considered sufficient. That said, there are some basic guiding principles to take into account when determining sample size, including the strength of the relationship between variables and factors (measured by level of communality), the number of factors, and the number of variables per factor. Smaller sample sizes are required when the communality is higher and the variable-to-factor ratio is higher (Mundfrom et al., 2005 ).

The current study assumes two general factors underlying fourteen measures, a variable to factor ratio of 7:1. According to the Monte Carlo study conducted by MacCallum et al. ( 1999 ), a sample size of 200 can achieve an excellent recovery when the communality is low, if the variable-to-factor ratio is 20:3 or greater. Inter-correlations between measures that are assumed to load on to one factor are expected to be significant. We were able to estimate pairwise correlations among six of our fourteen measures from previous studies in the TestMyBrain database. Some of these correlations were lower than 0.15. To detect a correlation of 0.15 with power of 0.80, the required sample size is 350. We therefore aimed to obtain 350 participants.

Recruitment

We recruited participants from three sources: Visitors to TestMyBrain.org, an online cognitive testing platform, who clicked on a link titled “Cancer and Cognition”; Constant Contact (secure email marketing service) email blasts to participants signed up for the Citizen Science 4 Brain Health community; advertisements shared with cancer-focused mailing lists including the National Cancer Institute’s email list, Cancer Carepoint, Gryt Health, Cancer Survivorship, and American Cancer Society’s network. Recruitment invitations included a brief study description and informed participants that they would get feedback on their scores after completing the study.

Inclusion/exclusion criteria

We included only participants between the ages of 18 and 89 at the time of recruitment. We excluded participants who had a disability that substantially interfered with their ability to complete neurocognitive tests and/or were a current resident of the European Union or European Economic Area. To satisfy the General Data Protection Regulation, the consent form stated that residents from the European Union or European Economic Area were not allowed to participate. Additionally, since we are interested in determining whether our results will generalize to a population of cancer survivors, the exclusion criteria for the current study included a current or past diagnosis of cancer. Data from participants with a current or past diagnosis of cancer will be reported in a separate paper.

Participants began the study by clicking on a link that took them to a study information/online consent form. Once they had read the information and agreed to the consent form, they were then directed to the study and given a link with a coded ID that they could use to access the study at a future time, if needed. Participants were not required to complete the study in a single session. Coded IDs were not linked with email addresses or other personal identifying information.

When participants first clicked on the link to the study, they were taken to a page that asked for their age and the type of device they were using. Participants who reported an age younger than 18 or older than 89 were not allowed to continue the study. Next, participants were informed that they would receive the results of their assessments once they completed the study. They were then asked a series of questions to ascertain their demographics (age, gender, race, ethnicity, educational background) and cancer history (diagnosis and treatment), if any.

Once they had answered these questions, they began the cognitive testing battery. The battery took around 90 min to complete. There were four possible testing orders, counterbalanced across participants. Time of completion of each test was captured in the data to allow for any performance differences that arose from participation across multiple sessions to be modeled in our data analysis.

After participants completed all measures, they were given a debriefing questionnaire which asked about any challenges or technical difficulties they may have experienced during testing. Finally, participants were presented with results of their assessment, based on comparing their scores to the scores of participants from the TestMyBrain normative database, along with links to resources to address any concerns they might have about their cognitive health, or any questions about cancer and cognition.

Measures were divided into those that were adapted from traditional tests of neuropsychological functioning and paradigms adapted from the experimental literature.

Neuropsychological tests

The Arithmetic test required participants to solve a series of 20 arithmetic word problems of increasing difficulty (e.g., “How many hours will it take to walk 24 miles at a rate of 3 miles per hour?”). For each trial, regardless of difficulty, the participant earned 1 point for a correct answer and 2 points for a correct answer given within 10 s. The primary outcome variable was total points earned across the test. The first 5 questions had a 15 s time limit, which increased to 30 s for questions 6–10, 60 s for questions 11–19, and 120 s for the final question. This test was modeled after the arithmetic test of the Wechsler Adult Intelligence Scale, 3rd Edition (Wechsler, 1997 ).

Trail making test, parts A and B

Participants were presented with a display of 25 circled alphanumeric characters. The task was to draw lines, on the device screen, connecting the circles in the proper order. In Part A, the circles contained digits that had to be connected in ascending numerical order (i.e., “1”, “2”, “3”… “25”) starting with “1”. In Part B, the circles contained both digits and letters that had to be connected in ascending numerical and alphabetical order, switching between digits and letters (i.e., “1”, “A”, “2”, “B”, “3”, “C”… “13”). Depending on the device (i.e., laptop/desktop or tablet/smartphone), participants could use either their mouse or their finger to connect the circles. As these two response types have different motor demands, we also corrected for device/input type in all analyses (Germine et al., 2019 ). The primary outcome variable for each part was the total time to connect all circles. This test was modeled after the classic Trail Making Test (Partington & Leiter, 1949 ; Reitan, 1971 ), adapted for digital administration.

Digit span, forward and backward

These tests required participants to recall sequences of digits of increasing length. Sequences were presented serially, one digit at a time. Each digit was presented at the center of the screen for 1 s. Then, the final digit was removed and the participant had to type in the sequence. For the Forward Digit Span, the sequence had to be typed in as it was originally presented. For the Backward Digit Span, the sequence had to be typed in reverse order. They had 4 s to respond. The test began with 2 digit sequences. There were two trials for each sequence length. If the participant was correct on at least one of the two trials, the sequence length was increased for the next trial, up to 11 digits. The test ended when the participant missed two trials of the same length. The primary outcome measure for both tests was the length of the longest sequence where participants got at least one of two trials right. These tests were modeled after the Digit Span Forward and Digit Span Backward tests from the Wechsler Adult Intelligence Scale, 3rd Edition (Wechsler, 1997 ). For details, see Hartshorne and Germine ( 2015 ).

Digit symbol coding

In this test, participants had to match a target symbol with its corresponding digit. On each trial, participants were shown a novel symbol at the top of the screen. Underneath the symbol was a key mapping each of 9 novel symbols to one of the digits 1–3. The participant had to type in the digit that corresponded to the target symbol. Participants had 90 s to complete as many trials as possible. The primary outcome measure was the number of correct trials. This test was modeled after the Digit Symbol Coding test from the Wechsler Adult Intelligence Scale, 3rd Edition (Wechsler, 1997 ). For details, see Hartshorne and Germine ( 2015 ).

Letter cancellation

The Letter Cancellation test was a search test where the target was a conjunction of the letter “d” and two line segments, and the distractors were the letter “d” with one, three, or four line segments, and the letter “p” with one to four line segments. The line segments could be above the letter, below the letter, or both. There were 57 letter + line segment items, arranged in a 6 × 10 grid with the rightmost three spaces on the bottom row blank. The task was to click on all instances of the target. Whenever the participant correctly clicked on a target, it turned red. There were 14 trials. Trials timed out after 20 s. The outcome variable was the total number of targets correctly detected. This test was modeled after the D2 Test (Brickenkamp & Zillmer, 1998 ).

Spatial span

In this test, participants saw an array of 16 circles, arranged in concentric groups of four around a central point. Circles were progressively larger moving out from the center. At the start of a trial, all of the circles flashed briefly. Then, a sequence of circles was flashed, one by one, followed by all of the circles flashing again. At this point, the participant had to click on the previously flashed circles in the proper sequence. They had 12 s to respond. The test began with sequences of length 4. There were two trials for each sequence length. If the participant was correct on at least one of the two trials, the sequence length was increased for the next trial, up to length 7. The test ended when the participant missed two trials of the same length. The primary outcome measure was the length of sequence the participant could accurately recall before making two consecutive mistakes. This test was modeled after the Corsi Spatial Span test (Corsi, P, 1972 ; Della Sala et al., 1999 ).

Experimental paradigms

Approximate number sense dots test.

On each trial, participants were shown an array of blue and yellow dots for 200 ms. There were 5–20 dots of each color, and dot size varied. The participant’s task was to report whether there were more blue dots or more yellow dots. Participants had 10 s to respond. There were 100 trials. The primary outcome measure was accuracy, defined as the proportion of correct responses. For details, see Halberda et al. ( 2008 , 2012 ).

Flanker interference

The Flanker paradigm required participants to indicate the direction of a central arrow flanked by arrows facing either the same direction (congruent) or the opposite direction (incongruent). The flanker arrows were displayed for 100 ms before target onset. The target and flankers were presented together for 50 ms, and then, all arrows disappeared and were replaced by a fixation cross for 70 ms. Participants were instructed to press the “x” key on their keyboard to report a left-pointing target or the “m” key to report a right-pointing target. Participants had three seconds to respond to each trial, and were instructed to respond as quickly as possible. On trials where a participant’s response time exceeded the 85th percentile of their incongruent trial response time distribution, they were warned to go faster. There were 96 trials. The primary outcome measure was the accuracy (proportion of correct trials) difference between congruent trials and incongruent trials. For details, see Passell et al. ( 2019 ).

Gradual onset continuous performance task (GradCPT)

In this task, the participant sees a series of 300 outdoor scenes, comprised of 90% street scenes and 10% mountain scenes, presented within a circular window. Images were presented for roughly 800 ms, and the transition between images was gradual. Participants were instructed to press a button when they saw a street scene, and to withhold response when they saw a mountain scene (10% of images). The primary outcome variable was d’ . To compute d’ , we used the commission error (CE) rate and omission error (OE) rates. The hit rate ( H ) was defined as 1-CE rate, or the proportion of target mountain scenes participants correctly withheld a response to. The false alarm rate ( F ) was the OE rate, or the number of non-target street scenes participants withheld responses to. We used the equation d’  =  z ( H ) −  z ( F ). In the cases where no CEs ( H  = 1.0) or OEs ( F  = 0.0) were made, we used the standard procedure (Macmillan & Creelman, 2005 ) of deducting or adding one-half error to each measure to prevent d’ from being undefined. For further methodological details, see Fortenbaugh et al. ( 2015 ).

Multiple object tracking (MOT)

This paradigm presented participants with an array of 10 identical black disks. At the beginning of each trial, a subset of disks would blink, alternating between a black and a green smiley face for 1000 ms to identify them as targets. All disks would then move randomly around the screen for 5 s. The participant’s task was to track the target disks. At the end of the trial, all disks stopped moving and the participant had 15 s to click on all of the target disks. Correct responses were indicated by green smiley faces, incorrect responses by red frowning faces. There were 3 sets of 6 trials, for a total of 18 trials. The number of targets increased from 3 in the first set to 4 in the second set to 5 in the third set. The primary outcome measure was accuracy, computed as the total number of correct responses divided by the total number of targets tracked. For details, see Passell et al. ( 2019 ) or Wilmer et al. ( 2016 ).

Spatial configuration visual search

This paradigm presented participants with an array of line segments arranged into “T” or “L” configurations. The participant’s task was to search for the target letter “T”, and report whether it was rotated 90° to the left or to the right. The letter “L” served as the distractor, and could appear in any of the four 90° rotations. There were two blocks of trials. In the first block, the total number of items (set size) was 4, and in the second block the set size was 12. Each trial had one target; the remaining items were distractors. Participants had 5 s to respond. There were 100 trials. The primary outcome measure was the slope of the reaction time (for correct trials only) by set size function.

Visual working memory (VWM)

In this test, participants were shown a memory array of four novel objects for 1 s. The objects then disappeared. After a 1000-ms retention interval, a single probe object was presented at one of the four locations, and participants were asked to make an unspeeded judgment as to whether or not the probe was the same object that was presented at that location in the original array. There were a total of six novel objects that were randomly sampled throughout the test. There were 4 practice trials and 42 test trials. The primary outcome measure was the number of correct responses (max = 42).

Data analysis

The data were cleaned and formatted for a series of factor analyses to understand the latent characteristics across the 14 measures. Scores were considered outliers if they were three times the interquartile range below the 25 th percentile or above the 75 th percentile of the outcome distribution. Participants with outliers on more than one measure were identified and excluded from the factor analysis. We also log-transformed those measures with highly skewed distributions.

While we had a priori expectations about how the experimental tests would relate to one another (all except perhaps GradCPT would load onto an a factor), the relationships among the neuropsychological tests and between the two classes of tests were left open. We therefore conducted an initial exploratory factor analysis, followed by a set of confirmatory factor analyses. Since our power analysis indicated at least 350 participants for the exploratory factor analysis, we randomly selected 55% of the participants (training group, n  = 357) to investigate the underlying factor structure. We held the remaining 45% of the participants (testing group, n  = 279) for the confirmatory factor analyses. The two groups showed no statistical difference on the performance of the 14 measures and on the demographic characteristics.

Exploratory factor analysis

We used several converging methods to determine how many factors to retain, beginning with a parallel analysis. Developed by (Horn, 1965 ), parallel analysis compares eigenvalues extracted from the analysis data against eigenvalues calculated from randomly generated correlation matrices using the same number of observations and variables. The number of factors to retain is determined by the number of eigenvalues from the analysis data that are larger than those that were randomly generated. The models with various number of factors were then compared in terms of the degree of fit assessed by three goodness-of-fit indices: Tucker–Lewis Index (TLI), root mean square of residuals (RMSR), and root mean square error of approximation (RMSEA), as well as the RMSEA 90% confidence interval (CI). A good fit is defined by TLI > 0.95, RMSR < 0.05, and RMSEA < 0.06 with lower value of CI close to 0 and upper value no more than 0.08 (Browne & Cudeck, 2016 ; Hu & Bentler, 1999 ). RMSR or RMSEA < 0.08 indicates an acceptable model fit.

Since all the measures are testing some aspects of cognitive ability, it would not be realistic to assume that any extracted latent structure is truly independent from the others. Therefore, we used maximum likelihood for eigenvalue calculation, factor extraction, and oblimin rotation when extracting more than one factor, to allow the factors to correlate.

Confirmatory factor analysis

Confirmatory factor analysis was performed using the held-out testing sample to assess and compare goodness-of-fit between the extracted factor structures and the other candidate structures. Based on prior literature, we hypothesized two factors, the general attention factor ( a ), and a sustained attention factor. We tested whether this hypothesis was supported by the observed data in terms of model fit indices such as comparative fit index (CFI, > 0.90 for acceptable fit, and > 0.95 for good fit) and standardized root mean square residual (SRMR, < 0.05 for good fit, and < 0.08 for acceptable fit, Hu & Bentler, 1999 ), in addition to TLI and RMSEA. Since the competing factor structures were not nested in nature, we followed the non-nested model comparison sequence as recommended by Merkle et al. ( 2016 ). We employed Vuong’s ( 1989 ) test to first determine whether the candidate models had equal fit to the same data, or whether the models were distinguishable. If they were distinguishable, we further tested whether one model fit significantly better than another using the non-nested likelihood ratio test (LRT). A final factor structure was distinguishable from the other candidate models and had acceptable model fit in both the exploratory factor analysis and confirmatory factor analysis.

Given the diverse sample and multiple ways to respond, we further assessed measurement invariance in the entire sample ( n  = 636) across demographic groups and response device groups (see Supplemental material in Thornton & Horowitz, 2020 ) using multigroup confirmatory factor analysis. The measurement invariance testing involves comparing models with increasing constraints. This begins with configural invariance, in which the same factorial structure is fitted to subgroups separately and factor loadings are allowed to vary freely (i.e., unconstrained model). Then, metric invariance (also called weak invariance) is tested by assessing the difference on goodness-of-fit indices of models imposing equality in factor loading across subgroups and the unconstrained models. If metric invariance holds, the next step is to test scalar invariance by further constraining intercepts to be equivalent across subgroups. The measurement invariance is determined by the insignificant changes (Δ) in model fit indices such as ΔCFI (≤ 0.01) and ΔRMSEA (≤ 0.015) (Cheung & Rensvold, 2002 ), especially ΔCFI which is more robust to sample size than chi-square (Δ χ 2 ).

Data manipulation and analyses were all conducted using R 3.6.0 (R Core Team, 2020 ). Exploratory factor analyses were obtained using the psych package (v1.8.12, Revelle, 2018 ), the lavaan package (v0.6-5, Rosseel, 2012 ) for the confirmatory factor analyses, nonnest2 (0.5-4, Merkle & You, 2020 ) for the Vuong tests, and semTools (v0.5-2, Jorgenson et al., 2019 ) to test for measurement invariance.

Recruitment and retention

As depicted in Fig.  1 , 4125 people completed the consent form and the demographic questionnaire (see below). Of those, 957 ended up completing the test battery, including 643 who reported not having a diagnosis of cancer. The latter group comprised the sample for this study. Seven people were found to have outliers on more than one measure and thus excluded from analysis. As a result, the analysis group contains 636 participants.

Study participation flow chart

Demographics

The basic demographic characteristics of the participants are shown in Table 1 . Participants were relatively young, but the majority were older than 25. A majority reported female gender. More than half had not finished college. Since the sample was web-based, we do not know participants’ geographic locations. Therefore, we cannot judge whether the racial/ethnic breakdown reflects larger populations (i.e., USA, the continent of North America, etc.). However, the proportion reporting an Asian race was larger than one would expect from a purely US sample.

Measure performance and correlations

Table 2 summarizes the outcomes used for each measure and their descriptive statistics. The two Trail Making Test tests (TMT-A, TMT-B) showed highly skewed distributions. After log-transformation, the distribution of these two tests was close to normal.

Figure  2 presents the correlation matrix of the 14 measures. Since scores for the two Trail Making Test tests and Visual Search were based on time, higher scores indicate poorer performance. In contrast, scores for the remaining measures were accuracy-based, and higher score indicates better performance. Therefore, we flipped the sign of the correlation coefficients for the time-based measures. While the most measures showed positive associations, the Flanker test, when measured by conflict accuracy, was weakly or even negatively correlated with the other tests.

Correlation matrix. Note that TMT-A and TMT-B are log-transformed, and the sign of correlation coefficients was flipped for TMT-A, TMT-B, and Visual Search

The exploratory factor analysis was conducted using the training group of 357 participants randomly selected from the full sample of 636. The parallel analysis shown in Fig.  3 suggested three factors. The first factor accounted for 34.5% of the total variance, and the first three factors together account for over 50% of the variance.

Parallel analysis scree plot

Next, we extracted one- to five-factor structures to compare goodness of fit. In addition to the three factor structure suggested by parallel analysis, the four- and five-factor structures also achieved good model fits (see Table 3 ).

Figure  4 shows the factor loadings of the five-factor structure, also depicted graphically in Fig.  5 . We named the five factors (1) Attentional Capacity, (2) Search, (3) Digit Span, (4) Arithmetic, and (5) Sustained Attention. All but one measure loaded highly on at least one of the five factors, using a minimum factor loading of 0.3. Flanker Inference had low loading on all five factors, which was not surprising given the low correlations observed in the correlation matrix. Although Digit Symbol Coding had a loading over 0.3 on the first (Capacity) factor, its loading onto the second (Search) factor was close to 0.3. Three factors had only one or two measures with high loadings, including the Digit Span factor, the Arithmetic factor, and the Sustained Attention factor (GradCPT only). The factor loadings for the one- to four-factor structures are available in “ Appendix B ”. In the three-factor structure, GradCPT loaded onto the first factor and Arithmetic loaded onto the second factor, while Visual Search did not load onto any factor. (Its loading on the first factor was 0.246, not meeting out 0.300 criterion.) In the four-factor structure, Arithmetic moved onto its own factor, while Visual Search loaded onto the second factor. Finally, the five-factor structure moved GradCPT onto the Sustained Attention factor. Table 4 shows the factor correlation matrix from the five-factor solution. Next, we used confirmatory factor analysis to compare model fit of the three exploratory factor structures and two other candidate structures.

Factor loadings from the five-factor exploratory factor analysis. Blue indicates positive loadings, red negative. The vertical line on each panel denotes the .30 threshold for inclusion of a measure in a given factor

Graphic depiction of five-factor exploratory factor analysis structure. Tests are disks on the outer circle, factors rectangles on the inner circle. Thickness of lines indicates factor loadings, green for positive, red for negative. Darker blue tests were cognitive paradigms predicted to load onto the a factor. The light blue test (GCP) was predicted to load onto sustained attention factor. Neuropsychological tests are depicted in green

Due to consistently low factor loadings on all the extracted factors, Flanker was excluded from the confirmatory factor analysis. We assessed the model fit for all factor solutions using the held out testing group of 279 participants. These included a one-factor solution in which all tests would load onto a general attention factor, and a two-factor solution where experimental and neuropsychological paradigms and tests clustered on independent factors, as well as the three, four, and five factor structures derived from the exploratory factor analysis.

Because the model comparisons require all models to be specified on the same set of measures, we kept Visual Search in the three-factor structure even though its highest factor loading was only 0.246. As shown in Table 5 , the three structures extracted from the exploratory factor analysis had much better model fit than the two a priori structures. The four-factor structure and the five-factor structure had slightly better model fit than the three-factor structure. The Vuong’s test indicated that the three-factor structure and the four-factor structure were distinguishable ( p  = 0.018), but they fitted the Testing group equally well based on the non-nested LRT ( p  = 0.060). The four-factor structure and the five-factor structure were indistinguishable ( p  = 0.375). The five-factor structure, however, is more theoretically plausible, since sustained and selective attention measures should be independent (Parasuraman et al., 1998 ).

Given the positive correlations between the five factors derived from the exploratory factor analysis (see Table 4 ), we further tested a hierarchical factor model (see Fig.  6 ) where a general factor was imposed above the five factors. The model fit was similar to that of the five-factor solution without the general factor. The Chi-square test also showed no significant difference ( p  = 0.076 for Δ χ 2 (Δ df  = 5) = 9.972) in terms of model fit between the two models. The model comparison result supported the existence of a general cognitive factor. However, the poor model fit of the single-factor structure in both exploratory and confirmatory factor analyses suggested that the five more-specific factors measure unique aspects of cognitive ability. Therefore, the five-factor structure was selected as the final model.

Graphic depiction of hierarchical factor model with five factors and the model with five inter-correlated factors

Measurement invariance

We collected information about the type of device participants used to respond for each measure. There were two general types of response mode, keyboard/mouse click and touchscreen. Participants were allowed to switch response devices between measures. To analyze the possible effect of response mode on the final factor structure, we included only participants who used one device consistently for all measures. There were 535 participants who used one device for all measures. Of these, 418 participants used either keyboard or mouse to respond, while 117 participants used touchscreen only.

The results showed that the metric invariance did not hold between the two mode groups (ΔCFI = 0.01, ΔRMSEA = 0.003, and p  < 0.01for Δ χ 2 (Δ df  = 8) = 25.694), indicating that individual factor loadings differed as a function of response mode.

Assessments of measurement invariance for demographic factors are reported in “ Appendix C ”—Measurement invariance analysis by demographic characteristics.

Both cognitive neuroscience and neuropsychology purport to measure cognitive functions, using a largely overlapping terminology (“attention”, executive function”, working memory”, etc.). However, the two fields are largely separate, with different goals and different institutional bases, and we know very little about how well concepts and measures from the two fields overlap. Our goal in this paper is to start making connections between the two fields in order to improve both neuropsychological assessment and our broader scientific understanding of cognition and the brain.

We hypothesized that the five selective experimental attention paradigms (ANS, MOT, VWM, Visual Search, and Flanker Interference) would load onto the a general attention factor reported by Huang et al. ( 2012 ). We could then use the degree to which the neuropsychological tests loaded onto a as an index of how well they function as (selective) attention measures. Neuropsychological tests that did not load on to a might load on to a common factor with the GradCPT, suggesting that they measure sustained attention. Or the neuropsychological tests might not be related at all to the experimental paradigms. The results that we actually observed were more complex than the scenarios we envisioned a priori. Our results are more consistent with a five-factor structure that can explain the observed correlations.

The five-factor structure

We settled on a five-factor structure, based on converging evidence from the scree plot, goodness-of-fit metrics, and theoretical considerations. These comprise: (1) an attentional capacity factor, (2) a search factor; (3) a digit span factor; (4) an arithmetic factor, and (5) a sustained attention factor. Flanker Interference did not load on to any factor.

The first factor comprised three experimental paradigms (MOT, VWM, and ANS), and two neuropsychological tests (Digit Symbol Coding and Spatial Span). Based on the nature of the three experimental paradigms, we tentatively label this the attentional capacity factor.

A second factor comprised the experimental Visual Search paradigm and three neuropsychological tests: Letter Cancellation, TMT-A, and TMT-B. All of the neuropsychological tests have a visual search component: the Letter Cancellation test requires the participant to look for the letter “d” with 2 lines among “d” with one or three lines and the letter “p” with one to three lines. It thus closely resembles a conjunction foraging search task (Jóhannesson et al., 2017 ; Kristjánsson et al., 2020 ). Both versions of the Trail Making Test require sequential search for alphanumeric characters. Therefore, we think of this factor as picking up variance related to search or attentional shifting. An important caveat here is that the configural Visual Search paradigm itself loaded less strongly onto this factor than the neuropsychological tests.

One interesting finding is that Digit Symbol Coding loaded almost equally on the first two factors. This is not entirely surprising given that there is a clear search component to the test. Participants need to find the target symbol in the key to find the correct response. Over the course of the test session, the key mappings will become automated and the search component will decrease in importance. If the mappings were to shift from trial to trial, this test would probably load more strongly on the search factor.

The third factor is fairly easy to characterize, as it included both the Forward Digit and Backward Digit Span tests, and nothing else. The Arithmetic test formed the fourth factor. These findings are in line with previous factor analytic studies of neuropsychological tests (Mirsky et al., 1991 ; Schmidt et al., 1994 ).

The fifth factor included just the GradCPT. We had predicted that this paradigm would not load onto the same factor as the other experimental cognitive paradigms, since it should measure sustained attention, rather than selective attention, and these faculties are known to be independent (Parasuraman et al., 1998 ). However, we did expect to see some of the neuropsychological tests to load onto this factor as well, which they did not.

A possible compromise between the single-factor and five-factor structures is the stratified model, where the five specific factors are nested under a general factor. The fit of this model was not statistically distinguishable from the five-factor model in our analyses, so it provides another way to look at the data. We assume that this general factor corresponds to something like the general intelligence g factor of the Cattell–Horn–Carroll model, rather than the general attention factor a proposed by Huang et al. ( 2012 ). In this context, it is worth noting that there was disagreement among the namesakes of the Cattell–Horn–Carroll model as to whether the general stratum was necessary (McGrew, 2009 ), and the recent work fitting the model to neuropsychological data eschew the general factor in favor of intercorrelated lower-order factors (Agelink van Rentergem et al., 2020 ; Jewsbury et al., 2017 ).

Relationship to other factor analyses of attention tasks

We assumed that our five selective attention paradigms would load on to a single factor, corresponding to Huang et al.’s (Huang et al., 2012 ) a . This was not what we observed. Only three paradigms MOT and VWM (and, if we’re generous, ANS) loaded into the first factor. Visual Search loaded onto the second factor with several of the neuropsychological tests, and Flanker Interference did not load onto any factor. This difference from Huang et al.’s analysis is not due to the fact that we found five factors, while Huang et al. found only one. If we limit ourselves to a single factor, Visual Search and Flanker Interference still do not load onto this factor (though all of the other tests do, see Table 6 ).

It is important to note that our study was not intended as a replication of Huang et al. (Huang et al., 2012 ). As we noted in the Introduction, there is no single definitive version of an experimental paradigm. The tests that we employed to instantiate the five paradigms we selected to represent the a factor differed in ways large and small from those used in the original Huang et al. study. In the MOT test, whereas our participants tracked 3–5 out of 10 items, Huang et al.’s tracked 4 of 8. Huang et al. measured VWM using a full-report technique, whereas we used a single-item probe technique. Our Visual Search paradigm was a search for a rotated T target among rotated Ls. Huang et al.’s Configuration Search paradigm was a search for a square composed of a white rectangle above a black rectangle among squares with the opposite spatial configuration. Instead of the even/odd judgment used in Huang et al.’s Counting test, our ANS test requires participants to judge which of two sets visible on the screen is larger. The dependent measure for Counting was a reaction time slope, as opposed to accuracy for the ANS. Furthermore, Huang et al.’s Counting task spanned the subitizing (3–4) and estimation ranges (13–14) ranges, while the ANS samples only the estimation range. As we noted in the Introduction, the Flanker Interference task is substantially different from Huang et al.’s Response Selection test.

Furthermore, factor analysis is sensitive to the context of the battery. Our battery included only four of the nine “primary” paradigms and none of the nine “secondary” paradigms used in Huang et al.’s battery. We also included the GradCPT and eight neuropsychological tests that were not in Huang et al.’s battery. This certainly affected the factor structure.

In contrast to Huang et al. ( 2012 )’s single factor solution, Skogsberg et al. ( 2015 ) obtained a four-factor structure for their battery: Global Attention, Sustained Attention, Transient Attention, and Spatiotemporal attention. Unfortunately, there are only two tasks in common between their battery and ours. Their Central Focusing Task corresponds to Flanker Interference, and both batteries included MOT. Furthermore, the reliability of the Central Focusing task was too low for it to be included in the analysis.

In the Skogsberg et al. ( 2015 ) data, MOT forms part of the Spatiotemporal Attention factor, so it is tempting to identify that with our Attentional Capacity factor. However, while MOT and the Spatial Span task fit that description, it is more difficult to see how VWM and Digit Symbol Coding can be thought of as spatiotemporal tasks. Furthermore, the ANS, which weakly loads onto our Capacity factor, would seem to correspond more closely to Skogsberg et al.’s Global Attention factor, since it requires the observer to segregate by color across the visual field. Meanwhile, Skogsberg et al.’s Spatiotemporal Attention factor includes the Spatial Shifting task, which we would predict should load onto with our Attentional Shifting Factor. Thus, our factor structure does not neatly align with Skogsberg et al.’s, although both analyses agree on the existence of a Sustained Attention Factor.

Similarly, it is difficult to map Huang et al.’s ( 2012 ) general factor to one of the four factors in Skogsberg et al. ( 2015 ). Again, the only paradigm in common between the two datasets is MOT, which would identify Huang et al.’s a with Skogsberg’s Spatiotemporal Attention factor, yet many of the paradigms in a do not fit that description (e.g., Visual Short-Term Memory, Response Selection, Counting). Perhaps it is our verbal descriptions of the factors that are misleading us here. It would be an interesting project, beyond the scope of this paper, to take the correlation matrices from these three studies (our study; Huang et al., 2012 ; and Skogsberg et al., 2015 ), subject them to the same factoring or clustering rules, and attempt some sort of synthesis. Ultimately, however, we are going to need more such studies, ideally using overlapping batteries. The existence of only three factor analytic studies of attention, with only one paradigm in common, points to the severe neglect of individual difference work in this field.

We also think it important to consider the demographic and cultural differences between the three samples. Both of the previous studies used convenience samples of undergraduate students. Huang et al. ( 2012 ) studied 257 students aged 17–22 at South China Normal University in Guangzhou, Guangdong, China. Skogsberg et al. ( 2015 ) studied 222 students, aged 18–26, from Northwestern University in Evanston, Illinois, USA. No demographic data were provided for participants in either study, though by definition they all possessed some college education.

Our study, in contrast, recruited a global internet sample of 636 people. Our participants ranged in age from 18 to 81, with a mean age of 31. More than half of our sample was older than the participants in the undergraduate studies. We also had much more variation in educational level, with 28% of our sample reporting a high school education or less, and 42% reported having completed a college degree. Finally, while we do not know which countries our participants lived in, only 21.1% reported Asian ethnic background, while 61.1% reported white or European ethnic background. Overall, we have good reasons to believe that there was a lot more demographic heterogeneity in our sample than in the two undergraduate samples.

Demographic characteristics seem likely to influence not only performance but also the observed factor structure. Our measurement invariance analysis (see “ Appendix C ”—Measurement invariance analysis by demographic characteristics) showed that metric invariance held for age, gender, and education, indicating that factor loadings did not significantly vary as a function of these characteristics. Nevertheless, we suggest that the greater diversity of our sample contributed to differences between the factor structure we observed and those obtained by previous studies, possibly via other characteristics that we did not consider here. Cultural variables may also influence the observed factor structures. Cross-cultural studies have indicated cultural differences in attention and perception between participants of East Asian and Western descent (Amer et al., 2017 ; Masuda & Nisbett, 2001 ). All of these issues need to be taken into account when comparing across studies or attempting theoretical generalizations. Now that remote testing platforms have become more widespread, future factor analytic studies should aim to cast a wider net, in order to increase both generalizability and variability.

Relationship to the Cattell–Horn–Carroll model of cognitive abilities

As we have mentioned, the Cattell–Horn–Carroll model (McGrew, 2009 ) is an influential model of human cognitive abilities, based in factor analysis. It arose out the field of intelligence measurement, and has recently been shown to fit well to neuropsychological batteries (Agelink van Rentergem et al., 2020 ; Jewsbury et al., 2017 ). The Cattell–Horn–Carroll model therefore provides a theoretically and empirically sound approach to classifying neuropsychological tests.

The relationship between Cattell–Horn–Carroll and the way cognition is thought of in cognitive psychology and cognitive science is not clear. Consider attention, the focus of this paper. Cattell–Horn–Carroll is a hierarchy of abilities, with narrow abilities (e.g., “quantitative reasoning”) organized under broad abilities (e.g., “fluid reasoning), with a general cognitive ability stratum at the top (McGrew & Schneider, 2018 ). There is no “broad ability” corresponding to attention in the Cattell–Horn–Carroll model. Attention is mentioned in many places in the hierarchy under fluid reasoning, working memory capacity, and processing speed ability. In this view, attention is not a single function or ability, but a property of many different subsystems. Jewsbury et al. ( 2017 ) proposed a similar view of executive function. Reconciling this approach with the view of attention as an independent factor or factor on its own will require cross-disciplinary collaboration.

Implications for theories of attention

Our analysis suggests three subcomponents of attention (i.e., attention capacity, search, and sustained attention). For the attention capacity factor, it is not surprising the experimental paradigms of MOT, VWM, and ANS paradigms comprised this factor. The relationship between visual working memory and multiple object tracking has been explored in some depth. It is important to keep in mind that spatial memory and visual working memory are distinct constructs (Klauer & Zhao, 2004 ; Oh & Kim, 2004 ; Vicari et al., 2003 ; Woodman & Luck, 2004 ). While the most intuitive model of multiple object tracking would involve storing the locations of targets in spatial memory, then moving attention in turn as quickly as possible to each target to update its location, this class of model is unable to account for MOT performance (Pylyshyn & Storm, 1988 ; Vul et al., 2009 ), leading theorists to propose additional cognitive structures or operations such as visual indexes (or “FINSTS” – “FINgers of INTsantiation”, Pylyshyn, 1989 , 2001 ) or multifocal attention (Cavanagh & Alvarez, 2005 ). There is also some dispute as to whether spatial working memory is involved. Allen, et al. ( 2006 ) argued that MOT performance was closely linked to Baddeley and Hitch’s spatial working memory store, aka the visuo-spatial sketchpad (Baddeley & Hitch, 1974 ). However, several studies have shown dissociations between MOT and spatial working memory (Bettencourt et al., 2011 ; Carter et al., 2005 ; O’Hearn et al., 2010 ). Furthermore, Trick et al. ( 2012 ) showed that visuospatial ability (including Corsi Blocks, a spatial span variant) but not working memory, predicts MOT.

The most striking link between the two paradigms is that they seem to have a similar capacity limit of around four items (Cowan, 2001 ). Vul et al. ( 2009 ) used an ideal observer model to show that the limit on the number of objects that can be tracked is not a property of the information available in the paradigm and therefore must derive from a limitation in either memory or attention. This analysis does not conclusively link MOT and VWM, but it does raise the possibility that their common variance might derive from reliance on a common attentional resource. Fougnie and Marois ( 2006 ) explicitly posited that the common capacity limit in the two paradigms (as well as rapid serial visual presentation paradigms) derived from a common reliance on visuospatial attention. However, Souza and Oberauer ( 2017 ) argued that VWM and MOT use different attentional resources.

Electrophysiological studies also demonstrate close links between VWM and MOT. The contralateral delay activity, a sustained voltage decrease during the retention interval of a short-term memory test, indexes the number of items held in visual working memory (Luck & Vogel, 1997 ; Luria et al., 2016 ). The amplitude of this activity can also be used to measure the number of targets being tracked in an MOT experiment (Drew & Vogel, 2008 ; Drew et al., 2011 , 2012 ). This suggests an overlap in the neural circuitry involved in the two paradigms.

While the relationship between ANS, on the one hand, and VWM and MOT, on the other, is not well studied, it is worth considering the theoretical relationship between enumeration and attention. Numerosity is just one of a set of summary or ensemble statistics that can be extracted by the visual system (Alvarez, 2011 ). There is some evidence these representations are derived independently from one another (Khvostov & Utochkin, 2019 ; Utochkin & Vostrikov, 2017 ). However, there may be some core faculty for computing statistics that is held in common, and is also useful for tracking and remembering objects. Alternatively, it may be that there is something unique about numerosity or magnitude perception that makes it a probe for attention.

Meanwhile, what does it mean that Visual Search did not load onto the first factor? Visual search has long been identified as a core paradigm in the modern study of attention, dating back to Treisman and Gelade’s seminal work ( 1980 ), yet here it does not load with other commonly used attentional paradigms. These results may be telling us something about the fractionation of attention. Performing a difficult spatial-configuration search with little guiding information will rely on directing focal attention to each item in turn (Moran et al., 2016 ; Woodman & Luck, 1999 ), and therefore much of the variance may be due primarily to variations in the speed with which participants can shift attention. A paradigm like the ANS, on the other hand, requires a global distribution of attention across the display, with no shifting. Similarly, most accounts of MOT assume that attention is continuously distributed to all items in parallel, rather than shifting from one target to another in turn (Howe et al., 2010 ; Pylyshyn & Storm, 1988 ), unless target identities also must be tracked (Oksama & Hyönä, 2008 ). Attention also seems to be required for maintenance in visual working memory (Balestrieri et al., 2019 ; Heuer & Schubö, 2016 ). In some cases, this involves discrete shifts of spatial attention (Williams et al., 2013 ); it is not clear if it is also possible to maintain attention to multiple items in parallel, as in MOT.

Implications for interpreting data from clinical research studies

The major impetus behind this project was our survey of meta-analyses of the neuropsychological research on cancer-related cognitive impairment (Horowitz et al., 2019 ). One of our findings was that there was a great deal of variability in how tests were assigned to domains. For example, the Digit Symbol Coding was classified as a test of processing speed 43.1% of the time, as an attention test 32.3% of the time, and as an executive function test 24.6% of the time. Furthermore, many tests classified as attention tests, such as Digit Span, seemed to us to have little face validity. This project was conceived as a way to provide some empirical guidance for what should be classified as an attention test and what should not, an approach that we hope will be adopted for other domains as well.

One conclusion from this study is that, in line with our initial impressions, Digit Span and Arithmetic tests should not be classified as attention tests. This is not a novel finding. Mirsky et al. ( 1991 ) conducted a factor analysis of putative neuropsychological attention tests, and found that Digit Span and Arithmetic tests did not load onto the same factor as Trail Making, Digit Symbol Coding, Letter Cancellation, Stroop and Continuous Performance tests. Digit Span and Arithmetic are probably best thought of as Working Memory tests, as specified in the Wechsler Adult Intelligence Scale standard model (Reynolds et al., 2013 ). Agelink ven Rentergen et al.’s ( 2020 ) factor analysis of neuropsychological tests also found that in the best-fitting model the Digit Span tests formed their own “working memory” factor.

On the positive side, we found evidence Digit Symbol Coding and Spatial Span do seem to be tapping attentional capacity, while Trail Making and Letter Cancellation measure attentional shifting. These five tests could continue to be classified as attention tests, on the basis of these results, though reports should distinguish between Capacity and Search ability.

Implications for future clinical research studies

The connection between a subset of the neuropsychological tests and the cognitive attention paradigms is a two-way street. Not only does this finding validate that the neuropsychological tests have some connection to the construct of attention, it also suggests that certain experimental paradigms might be usefully adapted to serve clinical purposes.

The standard armamentarium of clinical neuropsychology has a number of limitations (Marcopulos & Łojek, 2019 ), including lack of sensitivity, lack of process-purity (Kessels, 2019 ), and lack of repeatability (Bilder & Reise, 2019 ). Developing tests from cognitive neuroscience paradigms, which tend to be theoretically derived to be more process-pure, and designed for repeatability, is a potential solution (Kessels, 2019 ). Whether such tests would be more sensitive is an empirical question.

Experimental cognitive paradigms do have drawbacks as potential clinical tests (Kessels, 2019 ). Their psychometric properties, including sensitivity and reliability, are generally not known. Most paradigms have been tested primarily on college undergraduates, meaning not only is their generalizability in question, but also that without extensive norming studies, there is no way to adjust an individual’s score for factors like age, sex, and education. Determining clinical utility will require normative data with clinical and nonclinical samples. Many paradigms rely on response time, and may become unreliable when different populations adopt different speed-accuracy tradeoffs. Since each study adapts the paradigm to answer a specific question, there are innumerable variants of each paradigm, so there is no standard to adopt. And while they are typically not proprietary, by the same token they cannot simply be used off the shelf; some investment is necessary to produce a useful version.

We do not mean to minimize these problems. However, we do think that they can be overcome. The Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia initiative, for example, has been leveraging cognitive psychology and cognitive neuroscience paradigms to develop tests to improve our understanding of schizophrenia (Barch et al., 2009 ; Carter & Barch, 2007 ), and a similar initiative is underway for cognitive deficits associated with obesity and diabetes (d’Ardenne et al., 2019 ).

Our findings suggest that if a condition leads to deficits on the Digit Symbol Coding or Spatial Span tests, then a test based on MOT, for example, might be useful. Tullo et al. ( 2018 ) have begun developing MOT as a clinical test of attentional capacity. Similarly, if deficits are observed using the Trail Making Test, it might be worth using a visual search paradigm (Gold et al., 2007 ; Horowitz et al., 2006 ) to determine whether the problem stems specifically from a problem in shifting attention, or whether it might be attributable to the other faculties tapped by the Trail Making Test.

Limitations and caveats

There are a number of important limitations to this study. First, we are sampling from the population of people who are online and self-selected to participate in cognitive studies for free. As noted above, we think that our sample is probably more representative than, say, undergraduate university psychology students. However, it is better-educated, younger, and probably more affluent than the population as a whole. The majority of subjects were recruited through TestMyBrain.org. In past studies using TestMyBrain.org, the top sources for the site have been  www.google.com ,  www.stumbleupon.com , and  www.i-am-bored.com , and frequently used search terms leading to TestMyBrain.org were “brain test” and “brain tests”, suggesting that many visitors arrive at the website because they are curious about their cognitive abilities (Germine et al., 2012 ). Furthermore, we do not have a good idea of which countries our participants live in, and our demographic information does not line up with standard US racial/ethnic categories. Critically, we do not know how the factor structure might change for specific clinical populations. We are currently studying a population of cancer survivors, but interested researchers might want to carry out replications in their fields of interest.

Second, our analysis noted significant effects of response mode on the factor structure; whether participants used a keyboard and mouse or a touchscreen made a noticeable difference. We did not have enough participants who used only a touchscreen to fully characterize this effect, but since computing as a whole, and computerized neuropsychological testing in particular, is moving toward touchscreen interfaces, this issue will become increasingly important.

Perhaps the most important limitation of our study is that our neuropsychological tests were not necessarily identical to the tests currently being administered by clinical neuropsychologists. A challenge of the present study was converting traditional paper-and-pencil tests to an online format while keeping the differences between the two to a minimum. Instructions for TestMyBrain measures were given visually, and practices were completed in order to ensure comprehension of instructions. In contrast, a neuropsychologist administers the pencil-and-paper versions and instructions are given orally. The traditional Arithmetic and Digit Span tests require participants to verbally answer, the Trail Making Test, Digit Symbol Coding, and Letter Cancellation necessitate the use of a pen or pencil, and Spatial Span requires finger pointing. Our online measures were modified so that participants could respond using either a keyboard or touchscreen. In “ Appendix A ”—Comparison of online and traditional, we detail the traditional pencil-paper tests and their modified online counterparts. In addition to administration and formatting differences, digitizing pencil-and-pencil tests may alter the perceptional, cognitive and motor performances of tests and introduce measurement bias due to device variations (Germine et al., 2019 ).

Conclusions

The goal of this project is to provide a bridge between theory-driven cognitive research and clinically relevant neuropsychological research. We believe it is important to align neuropsychology with cognitive psychology and cognitive neuroscience to improve the precision and interpretability of cognitive assessments. Our results should provide guidance for which neuropsychological tests should be classified as attention tests, and hopefully provide inspiration for the development of new clinical assessments based on experimental attention paradigms. Furthermore, we hope we have provided a template for other researchers to explore the connections between cognitive paradigms and neuropsychological tests in domains beyond attention. By bringing these fields closer together, we can improve our scientific understanding of cognition, and ultimately improve the welfare of people who suffer from cognitive disorders and deficits.

Availability of data and materials

The datasets generated and/or analyzed during the current study are available in the Open Science Foundation repository, https://osf.io/py83d/ .

Abbreviations

Approximate number sense

Gradual onset continuous performance task

Multiple object tracking

Trail making test, version A

Trail making test, version B

Visual working memory

Comparative fit index

Likelihood ratio test

Root mean square of residuals

Root mean square error of approximation

Tucker–Lewis Index

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Acknowledgements

We would like to thank all of the participants for volunteering their time to complete the batter. We are grateful to the American Cancer Society, Cancer Carepoint, Gryt Health, and the National Coalition for Cancer Survivorship for helping to publicize this study.

Open Access funding provided by the National Institutes of Health (NIH). This project has been funded in part from the National Cancer Institute, under contract number # HHSN261201800002B to Westat. The opinions expressed by the authors are their own, and this material should not be interpreted as representing the official viewpoint of the U.S. Department of Health and Human Services, the National Institutes of Health or the National Cancer Institute.

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MT and TSH contributed to conceptualization. XZ, YYL, LSS and EP helped in data curation. XZ contributed to formal analysis. GCH and TSH contributed to funding acquisition. YYL, LSS and LG helped in investigation. MT, XZ, LG and TSH contributed to methodology. YYL and GCH helped in project administration. LG contributed to resources. LSS, EP and LG contributed to software. GCH, LG and TSH helped in supervision. XZ and TSH contributed to visualization. MT contributed to writing—original draft preparation. MT, XZ, LG and TSH helped in writing—review and editing. All authors read and approved the final manuscript.

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Appendix A: Comparison of online and traditional tests

TMT-A & TMT-B were similar between the online and paper and pencil version except for the implement used for connecting circles. In our online TMT-A & TMT-B, participants had to connect a series of circles on their device screen, in ascending order. For Form A, 25 circles contained numbers and had to be connected in ascending numerical order (e.g., 1-2-3) by drawing a line between circles. For Form B, 25 circles contained letters and numbers, and had to be connected in ascending numerical and alphabetical order, alternating between numbers and letters (e.g., 1-A-2-B-3-C). Depending on the device (i.e., laptop/desktop or tablet/smartphone), participants could use either their mouse or their finger to connect the circles. In the traditional Trail Making Test, participants are given a pencil and are asked to connect the circles. The primary outcome variable for both versions is the time it takes to connect all the circles, calculated separately for Parts A and B.

In the online Digit Symbol Coding test, participants had to choose which number matched a target symbol shown on screen, using a given symbol-number key. Participants could use either a keyboard or touchscreen. Participants in the paper and pencil version are given a pencil and a sheet of paper with the key located at the top of the page and rows of numbers. Participant are tasked to copy the symbol below each number. The primary measure for both online and original versions was how many matches the participant correctly makes in 90 s.

The online and paper and pencil Letter Cancellation tests are comparable in asking participants to search an array of “d” and “p” letters for instances of the lowercase letter “d” flanked by various arrangements of 2 lines. Anytime the participant saw the target letter, they were then asked to cross the letter out (by clicking or touching the letter for the online test or striking through the target letter with a pencil for the original version) until all instances of the letter were found or 20 s passed. Participants’ online score was the total number of correctly identified letters. Various calculations are derived from the traditional letter cancellation test but most notably, the sum of the number of target letters (Bates & Lemay, 2004 ).

The Arithmetic test required participants to solve a series of arithmetic computation and word problems of increasing difficulty. The online version was modeled after the arithmetic test of the Wechsler Adult Intelligence Scales III and was presented visually. For each online question, the participant could earn 1 point for a correct answer and 2 points for a correct answer given within 10 s, regardless of the time allowed or difficulty of the trial. The primary outcome variable was total points earned across the test. There was a total of 20 possible questions. Time limits were given for each question, 15 s for the first 5 questions, then 30 s for the next 5 questions, 60 s for the following 9 questions, and 120 s for the final question. For the traditional test, participants are orally presented with a series of word problems and are not supplied with a pencil or paper. Participants are timed beginning after each problem is read and participants must respond orally within a time limit. Time limits are 15 s for the first 6 questions, then 30 s for 7–11 questions, 60 s for 12 -19 questions, and 120 s for question 20. If the participant gets 4 consecutive wrong answers, then the test is stopped. For questions 1–18, 1 point is obtained for a correct answer given within the time limit; and for questions 9- 20, 2 points are obtained if a correct answer was given within 10 s or 1 point if answer was given within the time limit.

Both online and traditional digit span tests required participants to recall strings of digits of increasing length. The forward digit span test required participants to recall digits in the order they were presented. The backward digit span test required them to recall the digits in reverse order. The online tests were adapted from the Digit Span tests of the Wechsler Adult Intelligence Scales. For the online version, digit sequences are presented visually, and participants are asked to memorize the numbers and then either keyboard or finger press the digits. There are two trials for each sequence length presented and the test ends when the participant misses two trials of the same length. The longest possible sequence length is 11 digits. Participants have 4 s to respond before they are warned to keep responding with the remaining number of digits. Score for the digit span test is calculated as the highest number of digits participants were able to successfully recall at least once—in other words, the length of the longest sequence where participants got at least one of two trials right. For the traditional version, participants are orally presented the digit sequences and asked to verbally recite the sequence. The test ends when the two trials for the same sequence length is incorrect or when the maximal sequence length is reached (9 digits forward, 8 backward). Each correct response is worth one point.

In the Spatial Span test, participants had to learn and recall sequences of visually presented spatial locations. For the online test, sequences were indicated by a shape that changed color. When clicking on each dot in a sequence, participants have 12 s to click on the next dot. If at any point they take more than 12 s to click the next dot, they timeout and the next trial begins. The sequences increased in length from 4 to 7. The primary outcome measure was the length of sequence the participant could accurately recall before making two consecutive mistakes. For the traditional Spatial Span test, the administrator taps the spatial sequence on a board that contains an array of 10 blocks. Participants are then asked to reproduce the sequence by tapping the blocks in the same order they were presented. The test comprises eight sequence lengths, from 2 to 9, with two trials for each sequence length (Brown, 2016 ).

Appendix B: Factor loadings from the exploratory factor analysis

See Tables 6 and 7 .

Appendix C: Measurement invariance analysis by demographic characteristics

We assessed measurement invariance on three demographic characteristics. In addition to age group and gender, we further collapsed education into two subgroups, “Less than college” and “College and above” so each group had over 250 participants.

As shown in Table 8 , the results supported metric invariance for all three characteristics where the changes in fit indices were smaller than the recommended cutoff values for CFI and RMSEA as defined in the Confirmatory Factor Analysis subsection of the Data Analysis section. The scalar invariance held for education only in terms of ΔCFI and ΔRMSEA.

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Treviño, M., Zhu, X., Lu, Y.Y. et al. How do we measure attention? Using factor analysis to establish construct validity of neuropsychological tests. Cogn. Research 6 , 51 (2021). https://doi.org/10.1186/s41235-021-00313-1

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• Published: 30 November 2022

Decoding the cognitive states of attention and distraction in a real-life setting using EEG

• Pallavi Kaushik 1 , 2 ,
• Amir Moye 3 ,
• Marieke van Vugt 2 &
• Partha Pratim Roy 1  

Scientific Reports volume  12 , Article number:  20649 ( 2022 ) Cite this article

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Lapses in attention can have serious consequences in situations such as driving a car, hence there is considerable interest in tracking it using neural measures. However, as most of these studies have been done in highly controlled and artificial laboratory settings, we want to explore whether it is also possible to determine attention and distraction using electroencephalogram (EEG) data collected in a natural setting using machine/deep learning. 24 participants volunteered for the study. Data were collected from pairs of participants simultaneously while they engaged in Tibetan Monastic debate, a practice that is interesting because it is a real-life situation that generates substantial variability in attention states. We found that attention was on average associated with increased left frontal alpha, increased left parietal theta, and decreased central delta compared to distraction. In an attempt to predict attention and distraction, we found that a Long Short Term Memory model classified attention and distraction with maximum accuracy of 95.86% and 95.4% corresponding to delta and theta waves respectively. This study demonstrates that EEG data collected in a real-life setting can be used to predict attention states in participants with good accuracy, opening doors for developing Brain-Computer Interfaces that track attention in real-time using data extracted in daily life settings, rendering them much more usable.

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Introduction.

Attention plays a vital role in our day to day lives. It is essential for something as trivial as getting someone’s name right in a noisy place to something as crucial as avoiding making mistakes in a dangerous factory environment. Attention fluctuates over time 1 , and a sudden decrease can have disastrous consequences, for example leading to traffic accidents 2 , medical mistakes, factory incidents, etc. Given the importance of attention in everyday life, it would be helpful to track attentional states in real time. Various (neuro)physiological tools have been used for this end, including electroencephalography (EEG).

Mulholland 3 demonstrated that when a person was not paying attention, 9–14Hz alpha power in occipital electrodes was higher compared to when they were paying attention. Jin et al. 4 conducted a study with 18 participants using a \(128+6 (EOG)\) EEG device. The participants performed a sustained attention and visual search tasks and self-reports of attentional state were used for labeling. They reported an accuracy of approximately \(60\%\) using SVM for classifying self- reported attentional state, predominantly on the basis of occipital alpha oscillations. Another study by Mohamed et al. 5 conducted with 86 participants using a 14 channel (dry) EEG device reported the maximum accuracy of 70% with SVM, KNN and Gaussian Process for classifying attention into three classes (high, low and medium). The participants performed tests 6 containing activities that assessed attention, memory, perception, and other cognitive states. Awais et al. 1 performed a driving simulator and EEG (20 channels with dry electrodes) study with 22 participants to differentiate between attention and drowsy states. The study was conducted in three phases. Familiarization (no data collected) phase to familiarize the participants with the simulator. Training phase was next that familiarized the participants with the simulator for 10 min while wearing an EEG cap. Then was the monotonous Driving(MD) phase where the participants drove a car for 80 min at a speed of 80 km/h. Video recording during the MD phase was used to label instances of interest by identifying drowsiness-related events using from facial features, including eye blink duration, facial expressions, facial tone, eye blinking rate, and movements such as head-nodding and yawning. They also used self-rating on a Karolinska Sleepiness Scale (KSS) 7 at the start and end of the MD phase. They found that P3, P4, P7, P8, C3, Cz, O1 and O2 electrodes showed significant differences in signal amplitude between self-reported attentive and self-reported drowsy states. They also reported that EEG in every 10-min window showed differences in alpha, delta and theta frequency and SVM gave an accuracy of 80.60% in classifying alert and drowsy states.

Supplementary Table  1 provides a more detailed overview of these studies, which think form a representative sample of the broad literature. These findings have been replicated many times 4 , 5 , 8 but most of these studies involve recording the EEG signals in a lab, comparing conditions during which participants are distracted with external stimuli to conditions in which they are not. The common findings from these studies include:

Delta (0–4Hz), theta (4–8Hz) and alpha (8–13Hz) waves are most related to attention amongst all the brain waves. Various studies report increased mid-frontal theta, decreased central and parietal delta and decreased frontal and parietal alpha power with attention 1 , 4 , 9 , 10 , 11 , 12 .

Non-periodic EEG activity in the parietal, occipital and fronto-central regions of the brain being associated with attention 1 , 9 .

EEG signals corresponding to attention and distraction can be classified using machine learning with accuracies of up to 89% 10 , 13 .

(a) Framework of the recognition system used for classifying attention and distraction states. (b) Electrode positions as per the 10–20 International system belonging to Biosemi 32 channel EEG device used for data collection. (c) Experiment setup. EEG being recorded from both the participants simultaneously during a monastic debate with raters of attentional state in the background.

It is important to note that besides most of these studies use the EEG data collected in a laboratory setup while participants are given computerized tasks with carefully-controlled stimuli, constraints are also placed on the movement (should be avoided), speech (should also be avoided), etc of the participants. This means that the inferences obtained may not generalize to the real-life scenarios in which we want to track attention for preventing errors or optimizing performance. Another issue is that most of these studies interrupt the task to obtain self assessment reports from the participants to derive the instances of attention (on-task) and distraction (off-task for example, mind wandering) 4 , 9 , 14 . This not only disrupts the attention of the participant during the task but these self reports can be biased and/or unreliable.

As an exception to this, Ko et al. 15 conducted a semester-long EEG study with 18 participants in a classroom using a 32-channel gel-based EEG device. The data were collected while the participants had to detect special visual targets that occurred during regular university lectures as soon as possible. The authors reported that longer delays in reporting the visual targets were preceded by an increase in activity in the delta and theta bands and a decrease in activity in beta band over the occipital and temporal regions. Hence, while some studies 4 are based on reliable data and a high number of participants, they are conducted in a strict laboratory setting and/or require the participants themselves to indicate their mental state at each moment , others are done in more naturalistic settings but are relying on lower number of participants and/or low-quality EEG signals 13 , 14 .

In this study, we explore if attention can be tracked in real-time in a complex real-life social situation using the EEG data collected without imposing any constraints on the behaviour of the participants. We also explore if attentional states can be distinguished if the labels are based not on the judgements provided by the participant themselves, but instead by a second-person observer. This second-observer method is much less intrusive and can even be done by post-hoc analysis of videos recorded during the task. Our aim was to collect EEG in a naturalistic environment with high-quality EEG equipment. Monastic debate practised by Tibetan monks in India is one relatively naturalistic context in which substantial variation in focus and distraction occurs. It is a contemplative debating practice that is different from the Western debate not only in terms of its physical setting (Fig. 1 c) but also in its essence 16 . It is not aimed towards convincing the opponent of a standpoint but rather to find out inconsistencies in their reasoning. Monastic debates are accompanied by periods that are relatively boring since one goes through lines of reasoning systematically, as well as periods that are quite exciting when debaters tease each other or when they have almost demonstrated an inconsistency. We previously showed that monastic debate practice appears to enhance one’s neural signature of attentional focus, namely frontal midline theta power 16 , and therefore it is a good testing ground. Involving Tibetan monks as participants also helps us see if the findings can be generalised to a much different population than is usually studied (i.e., participants from Western industrialised countries) 17 . Monastic debate is explained in more detail in 16 .This study also tries to determine the differences (if those exist) in attention owing to experience of individuals in performing a task and if they can be captured by EEG data collected in a real-life scenario.

This section entails the description of the dataset, pre-processing, and other techniques used. The overall framework of the study can be seen in Fig. 1 a.

Participants

Given the limitations regarding budget and resources (specifically, the researchers had only limited time they could be present in the monastery in person for testing, and monks only had limited time available to participate in studies), data (which included not only EEG but also behavioral tasks and surveys) was collected from 24 participants. However, various other studies 12 , 18 , 19 have also used data from 24 participants for their study and have given robust results.

In this study we focus mainly on the EEG data. 24 male Tibetan monks from Sera Jey monastic university in Bylakuppe, India, were recruited as participants in this study. Though monastic debate is a common practice in Tibetan Buddhist monasteries, this monastery was chosen because it has a large population of monks that are highly skilled in this form of debate 16 . Participants were encouraged to do two rounds of debates, but some pairs had to leave the session after a single debate due to other engagements. The participants took part in a total of 46 debates and aged between 20 and 30 years old.

Ethics approval and consents

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The study protocol was approved by the CETO (Research Ethics Review Committee of the Faculty of Arts of the University of Groningen), protocol number 70890721. Oral informed consent was obtained from all individual participants included in the study. We chose to not use written informed consent because this would be very unfamiliar and anxiety provoking in this culture that is mainly oral. Permission for verbal consent was granted by CETO as well. It was emphasized that the participants could leave the study at any time if they desired to do so, without any repercussions. Informed consent was also taken for publication of identifying information/images in an online open-access publication.

EEG recordings

The EEG signals were recorded using a Biosemi 32 channel EEG system and the electrodes according to the 10–20 International system are shown in Fig. 1 b. Individual channels were adjusted until impedances were below 25 k \(\Omega \) 20 and CMS-DRL (common mode/driven-right leg—standard for a Biosemi EEG device) was used as the reference. The sampling rate was 512Hz. EEG caps were mounted on the heads of both the participants and signals were recorded during the entire duration of the debate from both of them simultaneously. This led to a large dataset with 92 (46*2) EEG recordings.

Manipulation of difficulty

To examine how topic difficulty affected the neural correlates of debate, and the amount of attention and distraction, debates were conducted on easy and more difficult topics 16 from their curriculum. There are a total of 46 debates (23 easy and 23 hard). Participants were assigned a specific debate topic and asked to debate for 10 min (easy debates) or 15 min (hard debates). These duration were chosen because they accord well with the natural duration of debates. However, because of the limited amount of data, the difficulty manipulation is not further examined in this study.

Variability in experience

Debaters with varying experience were involved in the study to examine the effects of debate training on cognitive and affective function. Monks were invited by class 12 . The class recruitment ensured that monks that have learnt how to debate, and have learnt the text we asked them to debate about. 10 more experienced participants with the monastic training of about 14–25 years and 14 less experienced participants with monastic training of 3–10 years participated in the study. Debaters were always paired with another debater who had the same level of experience because otherwise debates tend to not flow well.

Video recordings

Video recordings (with audio) of all the debates were done to aid in annotating the instances of interest. Hence, it was crucial for the timing in videos and EEG signals to be synchronised. EEG and video recordings were started together with a countdown to 3 for 32 debates. However, an added measure for synchronization was employed for the remaining 14 debates. The EEG and video recordings were started on countdown once when the participants were ready for debate but at the start of the debate a participant from the debating pair was asked to slowly blink 5 times. The blinks which are easily detected in the raw EEG signals provided the offset in time between the EEG and videos recordings to millisecond precision. Example video recordings have been uploaded online 21 , 22 .

Steps followed for pre-processing the collected EEG data.

Annotation of the data

To obtain ratings of attention and distraction, three senior monks, fluent in Tibetan and well versed in monastic debate observed the recorded videos. They used Behavioural Observation Research Interactive Software (BORIS) event logging software 23 to indicate when the debaters exhibited signs of either intense attention (staying relevant to the task) or serious distraction based on their utterances and their body language, like eye gaze (whether they are averted from the debater), facial expressions (like confusion, if they are consistent with the ongoing dialogue), and content of utterances (whether the participant is replying to the question asked, they asked to repeat the question because they did not listen) to determine if the participant’s response was not at all linked to the posed question, etc for annotation. These cues are similar to what we use in a normal conversation to see if a person is listening attentively or not, but narrowed down to the specific context of monastic debate. BORIS allows the rater to mark the start and end of the time interval corresponding to a certain category with a millisecond precision. These senior monks were all expert debaters with at least 15 years of training and were subsequently extensively briefed the meaning of the categories they had to mark. There were differences in rater annotations, with in particular large differences in the level of detail of labelling: some labelled minute changes, others more globally. To ameliorate these effects, we went with majority rating. We only labeled instances as ‘attention’ when at least half of the raters considered them ‘attention’, and ‘distraction’ when at least half of the raters considered them ‘distracted.’ In case a majority was not established we did not consider that instance during the analysis. Since the ratings involve the specification of time intervals, it is not possible to compute inter-rater reliability by means of the usual methods such as kappa. More details on annotation are presented in 12 and Supplementary Methods .

(a) t-value showing difference in mean amplitude (µV) between attention and distraction states in raw, delta, theta, alpha and beta brain waves data. Lines corresponding to 2.5 and − 2.5 are also shown for comprehension. (b) Topography plot of channels that show t-values of channels that had significant p-values of difference between attention and distraction in raw, delta, theta, alpha and beta brain waves after multiple correction using FDR (p-value threshold 0.0385). Channels with p-values above the threshold i.e. non-significant channels have been marked in red and corresponding t-values are plotted as zero for clarity.

Pre-processing

Figure 2 shows the pre-processing steps. The EEG signals were downsampled to 256 Hz and a 0.5–45 Hz bandpass filter was applied to remove high-frequency muscle activity 24 . Following this, ICA was done by visual inspection of the EEG signals and the topographic maps to remove artifacts such as eye blinks, saccades, muscle activities etc. (similar to 12 ). We also re-referenced the data to average reference which is preferred as it is a very stable referencing method 25 . To extract EEG segments corresponding to our events of interest, we extracted 2-s segments from EEG signals with 1 s prior to time indicated by the senior monks as reflecting focus and distraction. The reason for this timing is that it can account for the response time of the rater, which is on the order of 1 s. There are 142 and 46 segments of 2-s each corresponding to distraction and attention labels respectively in the easy debates and 160 and 138 segments of 2 s each corresponding to distraction and attention respectively in the hard debates. Thus, there are a total of 302 and 184 instances of distraction and attention, respectively.

Table 1 shows instances of attention and distraction per debate. These are the instances that the majority of annotators agreed upon and have been included in the study. On an average, there are 10 and 9 (rounded off) instances of attention and distraction per debate respectively. The range (maximum value − minimum value) of attention and distraction instances per debate is 32 and 35 respectively. Such substantial variation is expected, since some debates (conversations) are very engaging, while others are not.

Wavelet transform

The cognitive states we are considering are not strictly time-locked to a particular moment and therefore it makes most sense to focus on brain oscillations that are not so strongly time-locked. Moreover, brain oscillations have been linked most strongly to attention and distraction, especially those in the alpha and theta bands 4 . Various studies have found wavelet transform as a more efficient method for spectral analysis. 26 , 27 Daubechies (Db)-8 wavelet transform is used here 28 . A wavelet function at time t is shown in Eqs. ( 1 ) and ( 2 ), where \(a= 0,1,\ldots ,A'-1\) , \(t= 0,1,\ldots , T-1\) , \(A' = log_2(t)\) , \(b= 0,1,\ldots ,2^a-1\) , T is the length of the signal, \(a_{0}\) and \(b_{0}\) values are set to 2 and 1, respectively.

(a) Effect of debate experience on the frequency of occurrence of instances of attention and distraction. (b) Topography maps comparing alpha activity corresponding to the difference (µV) between attention and distraction states for less and more experienced participants.

The scaling and wavelet functions required for evaluating the Approximation (A) and Detail (D) coefficients is given in Eqs. ( 3 ) and ( 4 ), respectively.

The \(A_i\) and \(D_i\) coefficients at the \(i^{th}\) level are evaluated using Eqs. ( 5 ) and ( 6 ), respectively.

The Db-8 decomposition renders five wavelet coefficients corresponding to delta (0.5–4 Hz) 29 , theta (4–8 Hz) 30 , 31 , alpha (8–13 Hz) 32 , beta (13–28 Hz) 31 , and gamma (28-45 Hz) 31 brain waves. Since various studies have reported gamma waves to be associated with motor movement, and as no restrictions were placed on the movement of the participants in this study, those were excluded from further analysis 33 , 34 .

In this section we first examine what electrodes and frequency bands show differences between attention and distraction on average. Then, we examine whether it is possible to make use of these data to predict attention and distraction in real-time on a single-trial level using machine/deep learning.

Statistical differences between attention and distraction

In order to estimate the sufficient number of sample size for this study we conducted a power analysis. The reported values in 4 result in an effect size of 0.99, suggesting a minimum sample size of 11 at a power of 0.842. As the sample size for our study is 24 this results in a power of 0.91 which is sufficient to detect the difference between the two groups—attention and distraction.

An independent t-test (t \(\ge \) 4, threshold of 4 was chosen to reduce false positives) on the EEG data showed that in addition to alpha, significant differences between attention and distraction states exist in the raw, delta, theta, and beta frequency bands. Figure 3 a shows the t-values corresponding to the raw data and brain waves. However, an issue with EEG studies is that there are many electrodes and when statistical tests are done on each individual electrodes for each brain wave, that leads to an inflation of the false positive rate. To correct for this, we used the False Discovery Rate (FDR) 35 . A false discovery threshold level of 0.05 was used for the analysis which corresponds to the p-value threshold of 0.0385. Delta and alpha brain waves yielded the maximum number of channels that are significant in distinguishing attention from distraction. This analysis also shows that delta, alpha and theta brain waves have a more widespread area of channels that show a significant difference in activity between attention and distraction states. Delta and alpha brain waves have significant channels in the frontal and parietal areas of the brain and theta wave in right frontal, temporal and left parietal areas. The associated topographical maps with t-values of channels with significant p-values (after FDR) are shown in Fig. 3 b.

(a) LSTM architecture that predicted the attentional states with best accuracy. (b) Confusion matrix corresponding to test data in theta band for the dataset using LSTM which gave the maximum accuracy out of the various classifiers that were tested. (c) Comparison of classification accuracy on the basis of different data aspects for the test dataset. Delta (0.5–4 Hz) and theta (4–8 Hz) activity were most predictive of attentional state.

Differences in the states of attention and distraction associated with the differences in experience in the participants

It is of interest to know whether practice at a task (here: debate training) reduces distractability. To address this question we compare more and less experienced participants. The 23 easy debates in this dataset consisted of 11 debates amongst more experienced and 9 debates from less experienced participants. The 23 hard debates comprised of 7 and 14 debates between more and less experienced participants, respectively. It was found that both the hard and easy debates have more instances of distraction than attention. However, as one might expect, less experienced participants had more instances of distraction and fewer instances of attention than the experienced participants. Figure 4 a shows the count of these instances varying across the hard and easy debates amongst participants with different experience. A chi-square test ( \(\chi ^2\) (1) = 140.41, p < 2.2e−16) on these proportions (not taking the debate type into account) suggests that the proportions of attention vs distraction differ between beginners and more experienced participants.

Figure 4 b shows that for the alpha band, more experienced participants had higher activation in the occipital region when they were distracted as opposed to higher activation in temporal, occipital and frontal regions for the less experienced participants. Both the categories showed higher activation in the left pre-frontal region when they were rated to be attentive.

Machine learning to predict attention and distraction using EEG signals collected in a real life setting

Having established that there exist average differences between attention and distraction in the dataset, which predominantly manifest in right frontal channels, particularly in the alpha and theta bands, we then asked whether the mental state could be classified on a single-trial level in real-time. In order to classify the EEG signals as attention or distracted we experimented with various classifiers, Support Vector Machine (SVM), MultiLayer Perceptron (MLP) 36 , Random Forest (RF) 37 , 1D-Convolution Neural Network (CNN) 38 and Long Short Term Memory (LSTM) 39 . However, there is an imbalance in the dataset which has 340,480 samples for distraction as opposed to 164,864 samples for attention. This imbalance can easily lead to bias and lack of generalization in the classifiers. To avoid the bias, 200,000 instances of distraction were randomly sampled and used for classification.

For machine learning algorithms (MLP, SVM, RF) k-cross validation (k = 5,7, and 10) was used with k = 10 giving the maximum accuracy. Since hyper-parameter tuning is required for deep learning (CNN and LSTM), data were split into training, validation and test set in the ratio of 60:20:20 respectively 40 , 41 . We experimented with various models, trying different permutations of layers, regularizers, optimizers, batch size, window size and activation function. The models that performed the best are reported in the manuscript.

A 3-layer MLP with 16, 8 and 4 neurons in each layer, learning rate of 0.001, batch size 32, and Adam optimiser 42 (alpha = 0.001, beta1 = 0.9, and beta2 = 0.999) gave the highest accuracy of \(72.4\%\) in the beta wave. SVM with kernel as radial basis function gave the highest accuracy of about \(71\%\) in the alpha and beta wave. The ensembling technique of random forest of 50 decision trees each with maximum depth of 16 and Gini impurity criterion gave the maximum accuracy of 75% in the theta wave. 1D-CNN layer with 512 filters and 5 kernel size, followed by batch normalization 43 , dropout 44 (0.4) and a fully-connected layer with sigmoid activation gave the maximum accuracy of \(94.17\%\) in the theta wave. After experimentation the LSTM architecture in Fig. 5 a was found to give the best accuracy of accuracy of 95.86% and 95.4% for delta and theta waves. The learning rate, batch size, optimizer, sequence size for both reported models of 1D-CNN and LSTM were 0.001, 32, Adam 42 (alpha = 0.001, beta1 = 0.9, and beta2 = 0.999) and 32 respectively. Figure 5 c summarises the results obtained from various classifiers for each brain wave. LSTM predicts the states of distraction with a high accuracy of 99.2% but classifies approximately 8.4% of attention labels as distraction as shown in the confusion matrix corresponding to the theta wave in Fig. 5 b. Refer to the Supplementary Methods for more information on classifiers.

a) Topography maps of mean amplitudes (µV) corresponding to distraction state (left) and attention state (right) in alpha wave for dataset ’B’. b) Classification accuracy on the basis of different data aspects for the dataset ’B’ using the random forest classifier.

Towards more generalization

Precise annotation of a dataset plays a crucial role in classification using supervised learning. However, it is not always possible or easy to have these precise annotations when dealing with data acquired in a real-life scenario. In order to check if attention and distraction can be detected in a dataset that is not precisely annotated, we worked with a similar dataset (referred to as dataset ’B’ to avoid confusion) mentioned in 16 . It is a larger dataset with EEG data and video recordings from 55 Monastic debates. However, since that dataset was focused on finding differences between agreement and disagreement rather than focus and distraction, which were encoded incidentally made it less specific and accurate than dataset ’A’ reported above.

Moreover, it incorporated quite an inaccurate method to match the timing of EEG signals to the videos which can lead to some discrepancies in annotation. Dataset ’B’ showed differences in mean amplitudes between the two mental states but the topography maps did not show any noteworthy differences between various brain areas in raw or brain waves as shown in Fig. 6 a. Nevertheless a RF classifier of 20 trees with maximum depth of 20 still gave a maximum accuracy of 87% in the alpha and theta waves as shown in Fig. 6 b. As this dataset is noisy, random forest was chosen for classification as many studies have reported it to be robust to noisy data. 45 , 46 It is difficult to know whether the EEG signals corresponding to attention and distraction are sufficiently distinctive to be captured by machine learning even when the annotations are not as precise on the time axis or whether instead the classifier is picking up on noise in the EEG signals.

To address that question, we attempted to generalize between the classifiers of datasets ’A’ and ’B’. Specifically, we trained the random forest classifier on dataset ’B’ and tested it on the dataset ’A’. The classifier had 100 decision trees and each tree had a maximum depth of 40 but an accuracy of only 53% was obtained on the test set. Reversing the role of the datasets as train and test sets yielded a below-chance accuracy for the two mental states. It suggests that although the attention and distraction instances in the debaters in the individual datasets could be predicted, those predictions cannot be used to generalize these two cognitive states at least not for the datasets currently available. The transfer might be possible if a high-quality dataset were available.

Monastic debate was chosen as the task in our study as it is not only a less studied form of debate but also is a good testing ground for attention and distraction. We deliberately did not interrupt the flow of any debate or impose any restrains on the participants in terms of movement, speech, etc. so that the findings from the study can potentially be generalised to other real life scenarios like classrooms, tracking attention to study medical conditions such as Attention Deficit Hyperactivity Disorder (ADHD), etc.

Previous studies pertaining to understanding attention using EEG, found that theta and alpha bands are the most informative. Hence, we limited ourselves to these features. An important reason for doing so is the risk of overfitting in such a small dataset. It has been reported that adding too many features will inflate the false positives and lead to multiple comparison issues 47 , 48 . Additionally working with band waves is more interpretable by neuroscientists than features like mean, root mean square, etc., and thereby helps to understand the working of the brain.

We found that when participants were in an attentive state, their left frontal alpha had a higher amplitude compared to when they were distracted. Surprisingly, these findings are at odds with laboratory studies that associate attention mostly with decreased posterior alpha amplitude. A possible explanation for these findings could be that the distraction states that are observable overtly in a real-life situation are different from the self-reported distraction states in a controlled laboratory situation. However, in addition, attention was associated with decrease in central delta and increased left frontal theta wave amplitudes which has been reported by other studies as well 1 , 35 . This indicates the possibility of delta and theta brain waves being more reliable indicators of attention in laboratory and real-life settings, however, more studies need to be done to corroborate these results.

An important innovation in our study was to determine attentional state on the basis of ratings of videos, using a detailed rating scheme as opposed to self-reports which disrupt the attention of the participant from the task itself. The second-observer method used in this study has the benefit of not disturbing the debaters during the task. However, since attention and distraction are mental states known best to the person himself, only strong instances of attention and distraction could be annotated. Although this annotation method is more in line with how people figure out if a person is attentive or not in their day-to-day life, but, as one is more sure of someone being distracted than attentive, this resulted in a small dataset and a substantial imbalance in the dataset with relatively more instances of distraction reported than attention. To address this, one needs to ensure that the task is long enough that enough relevant samples have been collected. In our study, we found that 10 min of debates garnered enough samples to work with machine/deep learning.

It was also examined whether experience of an individual affected these mental states in terms of number of occurrences or their neural correlates. Not surprisingly, less experienced participants showed more instances of distraction both in easy and hard debates than more experienced monks consistent with the idea that debate helps to train attention 8 .

In order to identify the differences between attention and distraction, statistical analysis and machine/deep learning classification was carried out. The classification results were corroborated by statistical analysis, which also showed that delta, theta and alpha oscillations have most statistically significant electrodes that differentiate attention from distraction. It was found that LSTM classifier was best at predicting the instances of attention and distraction in the EEG data and obtained an accuracy of 95.86% and 95.4% in the delta and theta brain waves respectively. LSTMs outperforming other classifiers in predicting the subtle attention states using EEG is reassuring, given that LSTMs have been proven to work very well for predicting time-series data. This reinforces the idea that LSTMs are suitable for similar problems. This can prove to be a stepping stone in developing BCIs 49 , 50 that predict attention of users in real-time and in real-life scenarios like classrooms where the parameter of how attentive a student has been in class, is generally found by the grades they get. This system may be used in predicting if students are really attentive in the classrooms and hence can help in finding students who might need extra help. Such a BCI can potentially be used in conjunction with other sensors like EKG, etc and may be incorporated in vehicles so that vehicle can automatically stop (or go to self driving mode, etc) if the driver is distracted for a long duration. It can be used for factory workers where distraction leads to loss of lives or money so that such instances are detected and tended to before any loss is incurred. Nevertheless, before such exciting applications are possible, our findings should be replicated in different scenarios and populations.

With this study we tried to cover the gaps in previous studies conducted for differentiating between attention and distraction by working on EEG data collected from a real-life scenario of monastic debate with a high-quality 32-channel EEG recording system. It was found that on average the data showed significant differences between the states of attention and distraction suggesting that EEG data collected in real-life scenarios can help predict attention and distraction. Attention was on average associated with higher left frontal alpha and left parietal theta power. Attention was also associated with a decrease power in central electrodes in delta wave. Classification was performed with support vector machine, multilayer peceptron, random forest, 1-D CNN and LSTM. We found that the highest classification accuracy (approximately 95%) was observed with LSTM on the basis of delta and theta activity.

Data availability

Pre-processed EEG data can be downloaded from: https://unishare.nl/index.php/s/1UYBgoG7tF2xfqG Scripts are available at: https://github.com/kaushik-pallavi/scripts_monks .

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Acknowledgements

We sincerely thank the Sera Jey Science centre, India for their pivotal work in creating a labeling system for the behaviours observed during monastic debate practice. Members of the Sera Jey Science centre who were particularly pivotal to this work are Kalden Gyatso, Jampa Thakchoe, Lobsang Phuntsok, Jampa Khechok, Ngawang Norbu, and Jampa Gyaltsen. We also sincerely thank Bryce Johnson from Science for Monks and Nuns for facilitating the collaboration with the Sera Jey science centre and critical input on the development of the study design and the behaviour labeling system. We would also like to thank AFOSR REACH program (FA9550-18-1-0041) and SPARC grant (SPA-1360-CSE) for funding and facilitating this research.

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P.K. wrote the main manuscript text, carried out the pre-processing and the analysis. M.K. conceived the study and collected the data with A.M. M.K. and P.P.R. supervised the analysis. All authors discussed the results, and commented on the manuscript.

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Kaushik, P., Moye, A., Vugt, M.v. et al. Decoding the cognitive states of attention and distraction in a real-life setting using EEG. Sci Rep 12 , 20649 (2022). https://doi.org/10.1038/s41598-022-24417-w

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Cognitive Psychology of Attention: Foundations

• First Online: 01 January 2013

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Formal scientific inquiry into the processes underlying attention largely coincided with the emergence of the nascent field of cognitive psychology in the middle of the last century. As cognitive scientists developed models to explain how humans process sensory information, it was necessary to select certain salient stimuli, while ignoring stimuli with less informational value, thereby reducing the amount of input that has to be handled at any given moment in time. This selection process was conceptualized as “attentional” and considered to be an essential aspect of cognition. In this chapter, some of the major cognitive theories and experimental approaches to the study of attention will be discussed. The chapter begins with a review of information-processing theory and early cognitive studies of attention that focused on sensory selection. This will be followed by discussion of other early cognitive theories and approaches. In the next chapter we will review developments in the cognitive psychology of attention that have occurred since the first edition of this book (post-1990) and current status of the cognitive psychology of attention.

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Cohen, R.A. (2014). Cognitive Psychology of Attention: Foundations. In: The Neuropsychology of Attention. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-72639-7_3

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Experimental Studies of the Attention Processing Model in Multiple Object Tracking Task

Associated data.

The datasets presented in this article are not readily available be- cause the datasets involve unfinished research projects. If necessary, requests to access the datasets should be made to the corresponding author.

(1) Background: Attention is an important cognitive process in daily life. However, limited cognitive resources have been allocated to attention, especially for multiple objects and its mechanism is still unclear. Most of the previous studies have been based on the static attention paradigms with relatively lower ecological validity. Thus, we aimed to explore the attention processing mechanism in a multiple object tracking (MOT) task by using a dynamic attention paradigm. Two experiments were conducted to assess whether there was a multi-focus attention processing model, and whether the processing model changes with the number of target balls. (2) Methods: During the experiments, 33 university students completed MOT combined with the simultaneous–sequential paradigm, with tracking accuracy and reaction time of correct reaction as indicators. (3) Results: (i) When there were two target balls, an obvious bilateral field advantage was apparent. (ii) When there were four target balls, participants’ performance was significantly better when stimuli were presented simultaneously than when they were presented sequentially, showing a multi-focus attention processing model. (4) Conclusion: Attention processing is characterized by flexibility, providing strong evidence to support the multi-focus theory.

1. Introduction

With the continuous improvement in the level of social informatization, people’s intake of information is increasing, which occupies increasingly more cognitive resources. However, it has been agreed that our brain has limited capacity to process information at one time. Therefore, study on the mechanism of multi-object cognition is helpful to accurately select and process effective information. Attention plays an important role in the link between perception and cognition [ 1 ], and spatial attention has always been the focus in the field of attention [ 2 , 3 , 4 ], which refers to the process of allocating attention resources according to specific spatial information (e.g., location). Whether the brain can allocate attention to discrete spatial locations at the same time, or whether the focus of attention can be split, is one of the most important issues in the study of the attention processing mechanism. However, currently no consistent conclusion has been reached on this issue. Two key problems deserve attention. At first, the proper understanding of the processing mode of attention is critical for evaluating and training attention ability. Furthermore, attention mechanism has been widely used in various fields of artificial intelligence, such as object tracking, facial recognition, and further for self-driving cars and complex surveillance systems [ 5 ], which has been proven to be beneficial to improve the performance of the model. Bengio proposed that attention was a core element of “conscious” artificial intelligence in the report of ICLR 2020 [ 6 ]. Therefore, it is necessary to reveal the multi-object attention mechanism.

In 1980, Ponser put forward the “spotlight theory” (also referred to as attentional focus) [ 7 ], that creatively compared visual spatial attention to a spotlight, and suggests that an object in the spotlight is processed more effectively. It holds that attentional foci are distributed in a continuous spatial area and are inseparable [ 8 ]. Since then, the debate on whether the focus of attention could be split has begun. Several theories supported that the focus of attention was indivisible. For example, the model by Eriksen et al. (1974) also assumes that attention is distributed in a continuous spatial area [ 9 ]. Moreover, McCormick et al. (1998) found that most research has shown that the attention processing model still supports the “unified model” of attention (attention adjustment is limited to an inseparable continuous area) [ 10 ]. Further, several behavioral [ 9 , 11 , 12 , 13 , 14 ] electrophysiological [ 15 , 16 , 17 ] studies have supported the “unitary focus” attention processing model. In the past 15 years, however, more empirical studies have shown that the attention processing model can be parallel and “multi-focus”. For example, Awh et al. (2000) first found that study participants could allocate attention to 2–4 discontinuous positions with the method of partial report [ 18 ]. The experiment by Alvarez (2005) proved that when stimuli are simultaneously presented in the left and right visual hemifields, twice as many stimuli can be detected compared to when they are presented in a single visual hemifield. It was concluded that the attention process in the left and right visual hemifields are independent of each other. This effectively refuted the idea of a unitary focus, and put forward the multi-focus theory [ 19 ]. In a multi-object visual tracking task, a study participant can simultaneously track about four objects. Although this paradigm is different from the traditional attention allocation paradigm, some researchers believe that the ability to track multiple objects simultaneously supports the multi-focus attention processing model [ 20 , 21 ]. In addition, studies using event-related potentials and fMRI (functional magnetic resonance imaging) show that in tasks that require simultaneous attention allocation to multiple stimuli, the brain signals induced by the objects are enhanced, while the signals induced by a single distractor located between two objects are suppressed. This evidence shows the great flexibility of attention allocation [ 22 , 23 ], and opposes the unitary nature of attention focus. Although a growing number of studies supported the existence of multi-focus attention, researchers still believe that the multi-focus processing method focusing on multiple objects simultaneously consumes more attention resources and requires higher attention costs. Therefore, it cannot be the first choice in daily life. In the literature review by Jans et al. (2010), the following four criteria of focus splitting were put forward: (1) the task should be appropriately difficult; (2) the stimulus presentation time is short enough to prevent the generation of attention strategies (e.g., attention transfer); (3) there is appropriate clue-object interval: for simple tasks, short intervals should be adopted to prevent the early concentration and transfer of attention resources; for difficult tasks, long intervals should be adopted to ensure the thorough processing of clues; and (4) a complete assessment should be made of the attention of distractors surrounding the object to ensure they do not increase attention on the object. None of the 19 studies included in the review met all criteria [ 24 ]. Therefore, to date, there is no consistent conclusion regarding whether there is a multi-focus model during attention processing.

Since the 1980s, a large number of research based on the static attention paradigm has been accumulated in the field of spatial attention. However, attention exhibited a rhythmic and discrete temporal characteristic [ 25 , 26 , 27 ], with the temporal dimension involving dynamism. Therefore, it is necessary to reveal an attention processing mechanism with a dynamic paradigm. The multiple object tracking (MOT) task, proposed by Pylyshyn and Storm (1988) [ 21 ], is one of the classic paradigms for studying visual attention ability in dynamic scenes, which is often used to study the attention processing mechanism, attention allocation, and people’s differences in performance and training on MOT [ 28 ]. The classic MOT paradigm usually comprises three stages: (1) Cue: The cue stage involves the presentation of some simple objects with the same surface features (e.g., circles, squares, or the same letters). Some stimuli are marked as targets by flashing and changing some features, while others are distractors. (2) Movement: The cue disappears at this stage, all the objects start to move randomly and independently for several seconds, and observers are asked to track the marked targets during the cue stage; (3) Response: After several seconds of movement, movement stops and observers are asked to indicate which objects were targets (overall report) or whether a particular object was a target (partial report), and their response times and tracking accuracy rates are recorded (taking a target number of four as an example, as shown in Figure 1 ). Compared with traditional static attentional cognitive tasks, MOT has four characteristics of persistence, selectivity, dispersion, and dynamics [ 29 ], MOT is better aligned with the fact that people need to process a large quantity of information with limited attention resources, and it shows greater ecological validity [ 30 ]. Another paradigm used in this study is the simultaneous–sequential paradigm, which was first proposed by Eriksen and Spencer (1969) and used to study the processing speed of sensory–perceptual information [ 31 ]. Since then, many scholars have applied this paradigm to various studies regarding the resource constraints of visual perception and cognitive ability. For example, it was first used by Shiffrin and Gardner (1972) to study attentional ability [ 32 ]. In this paradigm, stimuli are presented in simultaneous and sequential time conditions: in the simultaneous condition, all stimuli are presented simultaneously while stimuli are presented successively in subsets in the sequential condition (as shown in Figure 2 ). Importantly, the presentation time of all stimuli is the same. The logic of this paradigm is that the processing load under simultaneous conditions is twice that under sequential conditions within the same processing time, if the performance of individual under simultaneous conditions is not worse than that under sequential conditions, it provides evidence for parallel, multi-focus processing.

Classical MOT task (target number is four).

Simultaneous–sequential paradigm.

To sum up, based on the literature review by Jans et al. (2010), there is still controversy about the existence of a multi-focus attention model, whether the foci of attention processing are split should be judged using sound criteria [ 24 ]. In addition, most of the previous studies on the attention processing model were based on the static attention cognitive paradigm, with relatively low ecological validity. Therefore, this study combined the MOT paradigm and the simultaneous–sequential paradigm and adapted the paradigms appropriately based on the criteria proposed by Jans et al. (2010) to study the existence of the multi-focus attention processing model with higher ecological validity ( Figure 3 ) [ 24 ]. The multi-focus attention model predicts that attention processing is parallel, and stimuli are processed simultaneously and independently. By comparison, a unitary-focus attention model predicts that attention processing is serial, and stimuli are processed successively. In the simultaneous condition, the number of stimuli to be processed at any one time is twice that of the sequential condition. If the multi-focus attention model does not exist and study participants cannot choose two or more nonadjacent positions at the same time, the simultaneous condition will be more difficult than the sequential condition. Therefore, in this study, it was assumed that the MOT ability is at least no worse when stimuli are presented simultaneously than that when stimuli are presented sequentially, and multi-focus attention exists. This study aimed to explore: (1) when there are two objects, whether there is bilateral independence in more difficult and higher ecological validity tasks, which could provide evidence for multi-focus patterns of attention; (2) the attention processing model when there are two objects; and (3) whether the attention processing model changes with an increased number of objects.

A Combination of MOT task and simultaneous–sequential paradigm.

2. Experiment I

2.1. methods, 2.1.1. participants.

A total of 33 students (17 males and 16 females) with an average age of 23.32 ± 3.03 years from a university in Shaanxi were recruited as research participants. All participants had normal vision or corrected vision, normal color perception, and were right-handed. All participants volunteered to participate in the experiment, they signed an informed consent form before the experiment commenced, and received a reward with small cash after it was completed.

2.1.2. Experiment Design

This study adopted a 2 (spatial conditions: between/within hemifields) ×2 (time conditions: simultaneous/sequential presentation) within-subjects design. The dependent variables were the accuracy and reaction times of participants. The tracking accuracy was the ratio of the number of objects correctly selected by participants to the total number of objects, and the total accuracy was the average of each trial. Only when the reaction was correct, the reaction time was included in the calculation. Both indicators were coded and calculated by MATLAB.

2.1.3. Instruments and Materials

All experimental programs were written by Matlab2017a and psychtoolbox (version 3). The stimuli were presented on a Dell P1917s display, with a 19-inch screen, screen resolution of 1280 × 1024 Hz, and vertical refresh rate of 60 Hz, which ensures smooth and clear presentation of stimuli. The whole experiment was completed in a quiet, well-lit laboratory. The participant sat in front of the screen, and his/her chin was fixed by a chin rest to ensure that the central axis of the head was aligned with the center of the screen, and the vertical distance between the eyes and the screen was 70 cm.

In the center of the screen, a square area separated by a black frame was presented as the tracking area both with a length and width of 35.2° visual angle. The stimulus materials used in the experiment included (1) a cross gaze point composed of vertical and horizontal line segments with a length of 2° visual angle; (2) balls with the same shape and size and a radius of 0.53° visual angle. Programming with the collision detection algorithm ensured the balls would not be blocked during the motor process, and would randomly change the motor direction after collision. The distance between the ball and the frame would not be less than a ball’s diameter.

2.1.4. Experimental Process

At the beginning of the experiment, instructions were presented on the screen, the subjects were asked to track the target balls that had been marked by turning red, and there was no further suggestive information. Then, an 80 trial adaptive stage was written by the QUEST program in psychtoolbox [ 33 , 34 ]. After each participant completed the adaptive task, a distribution chart was obtained, with the ordinate denoting the accuracy and the abscissa denoting the number of trials, which used a maximum likelihood method and a Bayesian algorithm to calculate the velocity of the ball when each subject achieved an accuracy of 75%. This stage took about 20 min to complete. Each participant participated in the formal experiment at the speed of the adaptive test, which consisted of 120 trials and took about 25 min. To avoid the generation of fatigue, the adaptive and formal experiments were completed in two separate sessions, and the interval was two weeks. In addition, an unlimited rest period occurred every 20 trials, and when the subjects finished their rest, they could press the button to continue the experiment.

The experimental paradigm was the combination of the MOT task and the simultaneous–sequential paradigm. The time condition was controlled by the sequence of stimulus presentation. In the simultaneous condition, the balls in four quadrants moved simultaneously, while the diagonal quadrants (the first and third quadrants, the second and fourth quadrants) moved in sequence in the sequential condition. The spatial condition was controlled by the quadrant of stimulus presentation, involving four situations as shown in Figure 4 :

Examples of four presentations of the target ball.

2.1.5. Statistical Analysis

First, the data were screened to exclude data with reaction times exceedingly more than 3 standard deviations from the mean so that the data for all participants conformed to a normal distribution. Second, the behavioral data were analyzed using the statistical software SPSS 22.0, mainly using the repeated measurement ANOVA.

2.2. Results

2.2.1. tracking accuracy.

Tracking accuracy rates in the different conditions are shown in Table 1 . Results of the repeated measurement analysis of variance (ANOVA) of two (spatial conditions: between visual hemifields/within a visual hemifield) × 2 (time conditions: simultaneous/sequential presentation) showed that the main effect of time was significant (F (1,30) = 11.929, p = 0.002, η p 2 = 0.284), and participants’ accuracy was higher in the sequential condition than in the simultaneous condition. Similarly, the main effect of the spatial condition was significant (F (1,30) = 60.377, p < 0.001, η p 2 = 0.668), and participants’ accuracy was higher when stimuli were presented between visual hemifields than that when stimuli were presented within a visual hemifield. The interaction between time and space was not significant ( Figure 5 ).

Comparison of tracing accuracy of different presentation conditions under simultaneous and sequential conditions. Note: *** indicates the significance level is p < 0.001.

Tracking accuracy (M ± SD, N = 33).

2.2.2. Tracking Reaction Time

The tracking reaction times in the different conditions are shown in Table 2 . Results of the repeated measurement ANOVA of 2 (spatial conditions: between visual hemifields/within a visual hemifield) × 2 (time conditions: simultaneous/sequential presentation) showed that the main effect of time was not significant for reaction times while the main effect of the spatial condition was significant (F (1,30) = 5.714, p = 0.023, η p 2 = 0.160), and participants’ reaction times were higher when stimuli were presented between visual hemifields than when they were presented within a visual hemifield. It can be seen that the interactions between the time and spatial conditions were also significant (F (1,30) = 5.196, p = 0.030, η p 2 = 0.148). Further simple effects analysis showed that when stimuli were presented between visual hemifields, the reaction times were lower when stimuli were presented simultaneously than when presented sequentially, and the difference was not statistically significant. By comparison, when stimuli were presented within a visual hemifield, the reaction times were higher when stimuli were presented simultaneously than when presented sequentially, and the difference was statistically significant (F (1,30) = 4.783, p = 0.037, η p 2 = 0.138) ( Figure 6 ).

Comparison of tracing reaction time of different presentation conditions under simultaneous and sequential conditions. Note: * indicates the significance level is p < 0.05.

Tracking reaction time t/ms (M ± SD, N = 33).

2.3. Summary

The results showed that, when the time condition was a simultaneous presentation, the accuracy of stimulus reactions presented between visual hemifields was significantly higher than when presented within a unitary visual hemifield, with a shorter reaction time, showing a clear bilateral field advantage [ 35 ]. In other words, when the information was distributed in the left and right visual hemifields, the processing performance was better than when it was presented in a unitary visual hemifield. However, when the time condition was the sequential presentation, there was no obvious bilateral advantage and the accuracy of stimulus reactions presented between visual hemifields was higher than when it was presented within a unitary visual hemifield, but the reaction time was longer. In this case, high accuracy was probably caused by slower reaction times. The possible reason is that in the sequential presentation, the participant only needs to process one object in each motor process, and the maximum threshold of attention resources is not reached, and high load is the premise of bilateral field advantage [ 35 ]. In Experiment I, the reaction performance in the simultaneous condition was significantly worse than in the sequential condition, and there was no evidence that directly supports the existence of the multi-focus attention model, which is inconsistent with previous research results [ 36 ]. One possible reason is that the attention resources were not completely occupied during the simultaneous or sequential tracking of two objects, which does not meet the premise of focus splitting—the concentration of attention resources [ 24 ]. Therefore, in Experiment II, the number of balls was increased to 4 and 6, to observe whether the multi-focus attention model would occur.

3. Experiment II

3.1. methods, 3.1.1. participants.

The same as Experiment I.

3.1.2. Experiment Design

This study adopted a 2 (time conditions: simultaneous/sequential presentation) × 3 (number of target balls: 2, 4, 6) within-subjects design. The dependent variables were the answer accuracy and reaction times of the participants. The tracking accuracy was the ratio of the number of targets correctly selected by participants to the total number of targets, and the total accuracy was the average of each trial.

3.1.3. Instruments and Materials

3.1.4. experimental process.

As in Experiment I, the experiment consisted of an adaptive test (80 trials) and a formal test (120 trials), with a total duration of about 50 min.

3.1.5. Statistical Analysis

3.2. results, 3.2.1. tracking accuracy.

Tracking accuracy rates in different conditions are shown in Table 3 . According to the repeated measurement variance analysis of 2 (time condition: simultaneous/sequential presentation) × 3 (number of target balls: 2/4/6) on the tracking accuracy, the main effect of time condition was significant (F (1,32) = 81.410, p < 0.001, η p 2 = 0.718; the main effect of the number of balls was significant (F (2,32) = 1125.138, p < 0.001, η p 2 = 0.972); the interaction between time condition and the number of balls was significant (F (2,64) = 347.826, p < 0.001, η p 2 = 0.916). The results of the simple effect test showed that when there were two targets, the tracking accuracy in the sequential condition was higher than that in the simultaneous condition, and the difference was statistically significant (F (1,32) = 61.371, p < 0.001, η p 2 = 0.657). When there were four targets, the tracking accuracy in the simultaneous condition was higher than that in the target condition, and the difference was statistically significant (F (1,32) = 445.060, p < 0.001, η p 2 = 0.933). When there were six targets, the tracking accuracy in the sequential condition was higher than that in the simultaneous condition, and the difference was statistically significant (F (1,32) = 19.765, p < 0.001, η p 2 = 0.382) ( Figure 7 ).

Comparison of the accuracy rate of different numbers of target balls in the simultaneous and sequential conditions. Note: *** indicates the significance level is p < 0.001.

3.2.2. Tracking Reaction Time

The tracking reaction time in different conditions is shown in Table 4 . According to the repeated measurement variance analysis of 2 (time condition: simultaneous/sequential presentation) × 6 (number of target balls: 2/4/6) on the reaction time, the main effect of time condition was not significant; the main effect of the number of balls was significant (F (1,32) = 11.690, p < 0.001, η p 2 = 0.430); the interaction between time condition and the number of balls was significant (F (1,32) = 9.461, p = 0.001, η p 2 = 0.379). Further simple effect analysis on the interaction shows that, when there were two targets, the reaction time in the sequential condition was lower than that in the simultaneous condition, and the difference was statistically significant (F (1,32) = 22.797, p < 0.001, η p 2 = 0.416). When there were four targets, the reaction time in the simultaneous condition was lower than that in the target condition, and the difference was statistically significant (F (1,32) = 8.682, p = 0.006, η p 2 = 0.213). When there were six targets, the reaction time in the sequential condition was lower than that in the simultaneous condition, although it was not statistically significant ( Figure 8 ).

Comparison of the accuracy rate of different numbers of target balls in simultaneous and sequential conditions. Note: ** indicates the significance level is p < 0.01.

3.3. Summary

The results showed that when there were two target balls, the tracking accuracy in the sequential condition was significantly higher than in the simultaneous condition, with shorter reaction times, which was consistent with the results in Experiment I. Thus, there was no evidence that directly supported the multi-focus attention model. When there were four targets, however, the tracking performance in the simultaneous condition was significantly better than in the sequential condition, which supported the existence of multi-focus attention. When the number of targets increased to six, the performance in the sequential condition was significantly better than in the simultaneous condition. With the increase in target balls, the attention model in the MOT task changed. In other words, the multi-focus attention model was only evident in this study when there were four targets.

4. Discussion

This study combined the MOT task and the simultaneous–sequential paradigm to study the attention processing model, and to explore whether there is a parallel and multi-focus processing model of stimuli when there are two objects, under the MOT task and in two stimulus presentation time conditions (Experiment I). An additional question was whether this processing model changed with more objects (Experiment II). The unitary-focus model predicts that in MOT tasks, only one object can be coded at a time, and by quickly switching attention foci among multiple objects [ 8 ], multiple objects are coded and processed. The multi-focus model predicts that individuals can process multiple objects both simultaneously and independently. Therefore, the logic of this paradigm is that the load of stimuli presented simultaneously in the two time conditions is twice that of stimuli presented sequentially, which makes it more difficult to track objects [ 32 ]. In view of this, the unitary-focus model predicts that stimuli presented simultaneously results in poorer performance, while the multi-focus model predicts that a person’s performance when stimuli are presented simultaneously is at least no worse than when stimuli are presented sequentially.

In Experiment I, the main effect of the spatial condition was significant, and the existence of bilateral field advantage was observed, which is consistent with previous research results [ 19 , 36 ]. There are independent attention resources in the two visual hemifields for visual tracking, and the attention process plays a key role in the emergence of a bilateral field advantage [ 37 ], which provides some evidence for the split of attention foci. The main effect of the time condition was also significant, and the performance in the sequential condition was significantly better than in the simultaneous condition. There was no evidence that directly supports the existence of a multi-focus attention processing model, which was contrary to the experimental expectation. The possible reason for this result is that during the MOT process, participants could track 4–5 balls simultaneously, with an accuracy rate of up to 85–95% [ 19 ]. In this part of experiment, there were only two objects, and the average accuracy reached 92.8%. The attention resources were not completely occupied, which does not meet the criteria for focus splitting—the concentration of attention resources [ 21 ]. Therefore, in Experiment II, the number of balls was increased to 4 and 6, to observe whether the attention processing model changed when resources were completely occupied and overloaded.

In Experiment II, the interaction between the time condition and the number of target balls was significant. When there were two target balls, the performance in the sequential condition was significantly better than in the simultaneous condition, which was inconsistent with previous research results [ 36 ]. One possible reason for this result is that the stimuli used in the study by Howe et al. (2010) were composed of lattices of four dots, and the mode of motion was one-dimensional clockwise or counterclockwise. In Experiment II, however, the motion of balls was two-dimensional, and the direction and speed of motion were random and unpredictable. When there were four target balls, the performance in the simultaneous condition was significantly better than in the sequential condition, which is consistent with previous research results and provides evidence that supports the multi-focus attention model [ 36 ]. When there were six target balls, the performance in the sequential condition was significantly better than in the simultaneous condition, and the attention processing model was changed into the unitary-focus and serial model. A possible reason for this result is that the number of balls exceeded the maximum attention threshold. In this case, individuals actively adopt strategies to track the targets to reach the highest efficiency, such as tracking adjacent target balls and abandoning the distant target balls. Results of Experiment II show that the attention processing model is not unitary and constant, but somewhat flexible.

It is obvious that working memory and visual spatial attention are strongly correlated [ 38 ]. In the present study, the working memory load of the subjects may be higher under the sequential condition than that under the simultaneous condition. According to the resource limitation theory, when two tasks need to occupy the same resources, the performance will decline [ 39 ], which may lead to another interpretation of the experimental results. However, previous study has shown that spatial and non-spatial working memory are dissociable functions of the brain [ 40 ]. Howard et al.’s study (2020) used MOT task, and found that for a purely spatial task, working memory and visual spatial attention appear to recruit separate capacity-limited resources [ 41 ]. Besides, several studies have also suggested that the resources of spatial working memory and visual spatial attention are independent of each other [ 42 , 43 , 44 , 45 ]. Further, Olivers proposed a model, which suggested that when the target is new on every trial, the effect of memory items is weak or absent [ 46 ], which is consistent with the situation in this study. Thus, we partially excluded the influence of working memory. Nevertheless, there is no denying of the strong relationship between working memory and attention. Therefore, the results may be confounded by the different components of working memory, and future research could further isolate the effect of this factor, such us combining event-related potential (ERP) and other technologies.

The current research has important theoretical implications for our understanding of visual processing, which can only be achieved by correctly understanding the processing mode of attention, and is the basis for the selection and training of attention ability of personnel with special occupational needs and further mechanism exploration. Moreover, this finding may be applied to the optimization of artificial intelligence models, such as object tracking.

5. Limitations

Despite the novelty of the current findings that indicate the attention processing mechanism is flexible, many strategies (such as switching and biased selection) can be adopted in the attention process. This study was based on behavioral data only, however, eye tracking and ERP technology can be added in future research to study the attention processing model more comprehensively and rule out the influence of working memory. Furthermore, we only examined the regular processing pattern of attention, but whether the processing helps track accuracy should be further investigated in future research.

6. Conclusions

In sum, evidence for the existence of a multi-focus and parallel attention processing model was found in the studies reported in this paper. When there were two target balls, there was an obvious bilateral field advantage. However, when the number of target balls just reached the threshold of four, the performance of study participants was clearly better when stimuli were presented simultaneously than when they were presented sequentially, providing strong evidence that supports the multi-focus theory.

Funding Statement

This research was supported by the Natural Science Foundation of Shaanxi Province, Grant Number. 2021JQ-335; and the Key project of PLA Logistics Research Program during the 14th Five-Year Plan period, Grant Number BKJ19J021.

Author Contributions

Data curation, Y.G., S.C., S.W., X.W. (Xiuchao Wang), X.W. (Xinlu Wang) and D.L.; funding acquisition, Y.G. and X.L.; investigation, D.L.; methodology, S.L. and X.W. (Xinlu Wang); software, S.L. and S.W.; writing—review and editing, S.L., Y.G. and X.L. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

This study was carried out in accordance with the Declaration of Helsinki and approved by the Ethics Committee for drug clinical trials of the First Affiliated Hospital of the Fourth Military Medical University (Project No. KY20222135-C-1).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Conflicts of interest.

The authors declare no conflict of interest.

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Mechanisms of attention: Psychophysics, cognitive psychology, and cognitive neuroscience

Affiliation.

• 1 Laboratory of Brain Processes (LOBES), Dana and David Dornsife Cognitive Neuroscience Imaging Center, and Departments of Psychology and Biomedical Engineering, University of Southern California, Los Angeles, CA 90089-1061, USA.
• PMID: 20523762
• PMCID: PMC2879667

Sensory physiologists and psychologists have recognized the importance of attention on human performance for more than 100 years. Since the 1970s, controlled and extensive experiments have examined effects of selective attention to a location in space or to an object. In addition to behavioral studies, cognitive neuroscientists have investigated the neural bases of attention. In this paper, I briefly review some classical attention paradigms, recent advances on the theory of attention, and some new insights from psychophysics and cognitive neuroscience. The focus is on the mechanisms of attention, that is, how attention improves human performance. Situations in which the perception of objects is unchanged, but performance may differ due to different decision structures, are distinguished from those in which attention changes the perceptual processes. The perceptual template model is introduced as a theoretical framework for analyzing mechanisms of attention. I also present empirical evidence for two attention mechanisms, stimulus enhancement and external noise exclusion, from psychophysics, neurophysiology and brain imaging.

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(a) The perceptual template model.…

(a) The perceptual template model. (b, c, d) Signatures of the three mechanisms…

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3 Chapter 3. Attention

CHAPTER 3: ATTENTION

We use the term “attention“ all the time, but what processes or abilities does that concept really refer to? This chapter will focus on how attention allows us to select certain parts of our environment and ignore other parts, and what happens to the ignored information. A key concept is the idea that we are limited in how much we can do at any one time. So we will also consider what happens when someone tries to do several things at once, such as driving while using electronic devices.

CHAPTER 3 LICENSE AND ATTRIBUTION

Source: Friedrich, F. (2019). Attention. In R. Biswas-Diener & E. Diener (Eds), Noba textbook series: Psychology . Champaign, IL: DEF publishers. Retrieved from http://noba.to/uv9x8df5

Attention by Frances Friedrich is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Condensed from original version; some content adapted to suit course. Cover photo by chuttersnap on Unsplash.

WHAT IS ATTENTION?

Before we begin exploring attention in its various forms, take a moment to consider how you think about the concept. How would you define attention, or how do you use the term? We certainly use the word very frequently in our everyday language: “ATTENTION! USE ONLY AS DIRECTED!” warns the label on the medicine bottle, meaning be alert to possible danger. “Pay attention!” pleads the weary seventh-grade teacher, not warning about danger (with possible exceptions, depending on the teacher) but urging the students to focus on the task at hand.

Are you reading these words right here right now? If so, it’s only because you directed your attention toward them. [Image: CC BY 2.0, https://goo.gl/BRvSA7]

We may refer to a child who is easily distracted as having an attention disorder, although we also are told that Americans have an attention span of about 8 seconds, down from 12 seconds in 2000, suggesting that we all have trouble sustaining concentration for any amount of time (from www.Statisticbrain.com). How that number was determined is not clear from the website, nor is it clear how attention span in the goldfish—9 seconds!—was measured, but the fact that our average span reportedly is less than that of a goldfish is intriguing, to say the least.

William James wrote extensively about attention in the late 1800s. An often quoted passage (James, 1890/1983) beautifully captures how intuitively obvious the concept of attention is, while it remains very difficult to define in measurable, concrete terms:

Everyone knows what attention is. It is the taking possession by the mind, in clear and vivid form, of one out of what seem several simultaneously possible objects or trains of thought. Focalization, concentration of consciousness are of its essence. It implies withdrawal from some things in order to deal effectively with others. (pp. 381–382)

Notice that this description touches on the conscious nature of attention, as well as the notion that what is in consciousness is often controlled voluntarily but can also be determined by events that capture our attention. Implied in this description is the idea that we seem to have a limited capacity for information processing, and that we can only attend to or be consciously aware of a small amount of information at any given time.

Many aspects of attention have been studied in the field of psychology. In some respects, we define different types of attention by the nature of the task used to study it. For example, a crucial issue in World War II was how long an individual could remain highly alert and accurate while watching a radar screen for enemy planes, and this problem led psychologists to study how attention works under such conditions. When watching for a rare event, it is easy to allow concentration to lag. (This a continues to be a challenge today for TSA agents, charged with looking at images of the contents of your carry-on items in search of knives, guns, or shampoo bottles larger than 3 oz.) Attention in the context of this type of search task refers to the level of sustained attention or vigilance one can maintain. In contrast, divided attention tasks allow us to determine how well individuals can attend to many sources of information at once. Spatial attention refers specifically to how we focus on one part of our environment and how we move attention to other locations in the environment. These are all examples of different aspects of attention, but an implied element of most of these ideas is the concept of selective attention ; some information is attended to while other information is intentionally blocked out. This module will focus on important issues in selective and divided attention, addressing these questions:

•           Can we pay attention to several sources of information at once, or do we have a limited capacity for information?

•           How do we select what to pay attention to?

•           What happens to information that we try to ignore?

•           Can we learn to divide attention between multiple tasks?

SELECTIVE ATTENTION 

THE COCKTAIL PARTY

Selective attention is the ability to select certain stimuli in the environment to process, while ignoring distracting information . One way to get an intuitive sense of how attention works is to consider situations in which attention is used. A party provides an excellent example for our purposes. Many people may be milling around, there is a dazzling variety of colors and sounds and smells, the buzz of many conversations is striking. There are so many conversations going on; how is it possible to select just one and follow it? You don’t have to be looking at the person talking; you may be listening with great interest to some gossip while pretending not to hear.

Beyond just hearing your name from the clamor at a party, other words or concepts, particularly unusual or significant ones to you, can also snag your attention. [Image: Ross, https://goo.gl/TVDfTn, CC BY-NC-SA 2.0, https://goo.gl/Toc0ZF]

However, once you are engaged in conversation with someone, you quickly become aware that you cannot also listen to other conversations at the same time. You also are probably not aware of how tight your shoes feel or of the smell of a nearby flower arrangement. On the other hand, if someone behind you mentions your name, you typically notice it immediately and may start attending to that (much more interesting) conversation. This situation highlights an interesting set of observations. We have an amazing ability to select and track one voice, visual object, etc., even when a million things are competing for our attention, but at the same time, we seem to be limited in how much we can attend to at one time, which in turn suggests that attention is crucial in selecting what is important. How does it all work?

DICHOTIC LISTENING STUDIES

This cocktail party scenario is the quintessential example of selective attention, and it is essentially what some early researchers tried to replicate under controlled laboratory conditions as a starting point for understanding the role of attention in perception (e.g., Cherry, 1953; Moray, 1959). In particular, they used dichotic listening and shadowing tasks to evaluate the selection process. Dichotic listening simply refers to the situation when two messages are presented simultaneously to an individual, with one message in each ear. In order to control which message the person attends to, the individual is asked to repeat back or “shadow” one of the messages as he hears it. For example, let’s say that a story about a camping trip is presented to John’s left ear, and a story about Abe Lincoln is presented to his right ear. The typical dichotic listening task would have John repeat the story presented to one ear as he hears it. Can he do that without being distracted by the information in the other ear?

People can become pretty good at the shadowing task, and they can easily report the content of the message that they attend to. But what happens to the ignored message? Typically, people can tell you if the ignored message was a man’s or a woman’s voice, or other physical characteristics of the speech, but they cannot tell you what the message was about. In fact, many studies have shown that people in a shadowing task were not aware of a change in the language of the message (e.g., from English to German; Cherry, 1953), and they didn’t even notice when the same word was repeated in the unattended ear more than 35 times (Moray, 1959)! Only the basic physical characteristics, such as the pitch of the unattended message, could be reported.

On the basis of these types of experiments, it seems that we can answer the first question about how much information we can attend to very easily: not very much. We clearly have a limited capacity for processing information for meaning, making the selection process all the more important. The question becomes: How does this selection process work?

MODELS OF SELECTIVE ATTENTION

Broadbent’s Filter Model. Many researchers have investigated how selection occurs and what happens to ignored information. Donald Broadbent was one of the first to try to characterize the selection process. His Filter Model was based on the dichotic listening tasks described above as well as other types of experiments (Broadbent, 1958). He found that people select information on the basis of physical features : the sensory channel (or ear) that a message was coming in, the pitch of the voice, the color or font of a visual message. People seemed vaguely aware of the physical features of the unattended information, but had no knowledge of the meaning. As a result, Broadbent argued that selection occurs very early , with no additional processing for the unselected information. A flowchart of the model might look like this:

Figure 1: This figure shows information going in both the left and right ears. Some basic sensory information, such as pitch, is processed, but the filter only allows the information from one ear to be processed further. Only the information from the left ear is transferred to short-term memory (STM) and conscious awareness, and then further processed for meaning. That means that the ignored information never makes it beyond a basic physical analysis.

TREISMAN’S ATTENTUATION MODEL

Broadbent’s model makes sense, but if you think about it you already know that it cannot account for all aspects of the Cocktail Party Effect. What doesn’t fit? The fact is that you tend to hear your own name when it is spoken by someone, even if you are deeply engaged in a conversation. We mentioned earlier that people in a shadowing experiment were unaware of a word in the unattended ear that was repeated many times—and yet many people noticed their own name in the unattended ear even it occurred only once.

Anne Treisman (1960) carried out a number of dichotic listening experiments in which she presented two different stories to the two ears. As usual, she asked people to shadow the message in one ear. As the stories progressed, however, she switched the stories to the opposite ears. Treisman found that individuals spontaneously followed the story, or the content of the message, when it shifted from the left ear to the right ear. Then they realized they were shadowing the wrong ear and switched back.

Results like this, and the fact that you tend to hear meaningful information even when you aren’t paying attention to it, suggest that we do monitor the unattended information to some degree on the basis of its meaning. Therefore, the filter theory can’t be right to suggest that unattended information is completely blocked at the sensory analysis level. Instead, Treisman suggested that selection starts at the physical or perceptual level, but that the unattended information is not blocked completely, it is just weakened or attenuated . As a result, highly meaningful or pertinent information in the unattended ear will get through the filter for further processing at the level of meaning. Figure 2 shows information going in both ears, and in this case there is no filter that completely blocks nonselected information. Instead, selection of the left ear information strengthens that material, while the nonselected information in the right ear is weakened. However, if the preliminary analysis shows that the nonselected information is especially pertinent or meaningful (such as your own name), then the Attenuation Control will instead strengthen the more meaningful information.

Figure 2: Early selection model

LATE SELECTION MODELS

Other selective attention models have been proposed as well. A late selection or response selection model proposed by Deutsch and Deutsch (1963) suggests that all information in the unattended ear is processed on the basis of meaning, not just the selected or highly pertinent information. However, only the information that is relevant for the task response gets into conscious awareness. This model is consistent with ideas of subliminal perception; in other words, that you don’t have to be aware of or attending a message for it to be fully processed for meaning.

Figure 3: Late selection model

You might notice that Figure 3 looks a lot like the Early Selection model—only the location of the selective filter has changed, with the assumption that analysis of meaning occurs before selection occurs, but only the selected information becomes conscious.

MULTIMODE MODEL

Why did researchers keep coming up with different models? Because no model really seemed to account for all the data, some of which indicates that nonselected information is blocked completely, whereas other studies suggest that it can be processed for meaning. The multimode model addresses this apparent inconsistency, suggesting that the stage at which selection occurs can change depending on the task. Johnston and Heinz (1978) demonstrated that under some conditions, we can select what to attend to at a very early stage and we do not process the content of the unattended message very much at all. Analyzing physical information, such as attending to information based on whether it is a male or female voice, is relatively easy; it occurs automatically, rapidly, and doesn’t take much effort. Under the right conditions, we can select what to attend to on the basis of the meaning of the messages.

However, the late selection option—processing the content of all messages before selection— is more difficult and requires more effort. The benefit, though, is that we have the flexibility to change how we deploy our attention depending upon what we are trying to accomplish, which is one of the greatest strengths of our cognitive system.

This discussion of selective attention has focused on experiments using auditory material, but the same principles hold for other perceptual systems as well. Neisser (1979) investigated some of the same questions with visual materials by superimposing two semi-transparent video clips and asking viewers to attend to just one series of actions. As with the auditory materials, viewers often were unaware of what went on in the other clearly visible video.

Twenty years later, Simons and Chabris (1999) explored and expanded these findings using similar techniques, and triggered a flood of new work in an area referred to as inattentional blindness.

SUBLIMINAL PERCEPTION

The idea of subliminal perception—that stimuli presented below the threshold for awareness can influence thoughts, feelings, or actions—is a fascinating and kind of creepy one. Can messages you are unaware of, embedded in movies or ads or the music playing in the grocery store, really influence what you buy? Many such claims of the power of subliminal perception have been made. One of the most famous came from a market researcher who claimed that the message “Eat Popcorn” briefly flashed throughout a movie increased popcorn sales by more than 50%, although he later admitted that the study was made up (Merikle, 2000). Psychologists have worked hard to investigate whether this is a valid phenomenon. Studying subliminal perception is more difficult than it might seem, because of the difficulty of establishing what the threshold for consciousness is or of even determining what type of threshold is important; for example, Cheesman and Merikle (1984, 1986) make an important distinction between objective and subjective thresholds. The bottom line is that there is some evidence that individuals can be influenced by stimuli they are not aware of, but how complex the stimuli can be or the extent to which unconscious material can affect behavior is not settled (e.g., Bargh & Morsella, 2008; Greenwald, 1992; Merikle, 2000).

DIVIDED ATTENTION & MULTITASKING

In spite of the evidence of our limited capacity, we all like to think that we can do several things at once. Some people claim to be able to multitask without any problem: reading a textbook while watching television and talking with friends; talking on the phone while playing computer games; texting while driving. The fact is that we sometimes can seem to juggle several things at once, but the question remains whether dividing attention in this way impairs performance.

Is it possible to overcome the limited capacity that we experience when engaging in cognitive tasks? We know that with extensive practice, we can acquire skills that do not appear to require conscious attention. As we walk down the street, we don’t need to think consciously about what muscle to contract in order to take the next step. Indeed, paying attention to automated skills can lead to a breakdown in performance, or “choking” (e.g., Beilock & Carr, 2001). But what about higher level, more mentally demanding tasks: Is it possible to learn to perform two complex tasks at the same time?

DIVIDED ATTENTION TASKS

Unless a task is fully automated, some researchers suggest that “multi-tasking” doesn’t really exist; you are just rapidly switching your attention back and forth between tasks. [Image: CC0 Public

Domain, https://goo.gl/ m25gce]

In a classic study that examined this type of divided attention task, two participants were trained to take dictation for spoken words while reading unrelated material for comprehension (Spelke, Hirst, & Neisser, 1976). In divided attention tasks such as these, each task is evaluated separately, in order to determine baseline performance when the individual can allocate as many cognitive resources as necessary to one task at a time. Then performance is evaluated when the two tasks are performed simultaneously. A decrease in performance for either task would suggest that even if attention can be divided or switched between the tasks, the cognitive demands are too great to avoid disruption of performance. (We should note here that divided attention tasks are designed, in principle, to see if two tasks can be carried out simultaneously. A related research area looks at task switching and how well we can switch back and forth among different tasks [e.g., Monsell, 2003]. It turns out that switching itself is cognitively demanding and can impair performance.)

The focus of the Spelke et al. (1976) study was whether individuals could learn to perform two relatively complex tasks concurrently, without impairing performance. The participants received plenty of practice—the study lasted 17 weeks and they had a 1-hour session each day, 5 days a week. These participants were able to learn to take dictation for lists of words and read for comprehension without affecting performance in either task, and the authors suggested that perhaps there are not fixed limits on our attentional capacity. However, changing the tasks somewhat, such as reading aloud rather than silently, impaired performance initially, so this multitasking ability may be specific to these well-learned tasks. Indeed, not everyone could learn to perform two complex tasks without performance costs (Hirst, Neisser, & Spelke, 1978), although the fact that some can is impressive.

DISTRACTED DRIVING

More relevant to our current lifestyles are questions about multitasking while texting or having cell phone conversations. Research designed to investigate, under controlled conditions, multitasking while driving has revealed some surprising results. Certainly there are many possible types of distractions that could impair driving performance, such as applying makeup using the rearview mirror, attempting (usually in vain) to stop the kids in the backseat from fighting, fiddling with the CD player, trying to negotiate a handheld cell phone, a cigarette, and a soda all at once, eating a bowl of cereal while driving (!). But we tend to have a strong sense that we CAN multitask while driving, and cars are being built with more and more technological capabilities that encourage multitasking. How good are we at dividing attention in these cases?

If you look at your phone for just 5 seconds while driving at 55mph, that means you have driven the length of a football field without looking at the road. [Image: CC0 Public Domain, https://goo.gl/m25gce]

Most people acknowledge the distraction caused by texting while driving and the reason seems obvious: Your eyes are off the road and your hands and at least one hand (often both) are engaged while texting. However, the problem is not simply one of occupied hands or eyes, but rather that the cognitive demands on our limited capacity systems can seriously impair driving performance (Strayer, Watson, & Drews, 2011). The effect of a cell phone conversation on performance (such as not noticing someone’s brake lights or responding more slowly to them) is just as significant when the individual is having a conversation with a hands-free device as with a handheld phone; the same impairments do not occur when listening to the radio or a book on tape (Strayer & Johnston, 2001). Moreover, studies using eye-tracking devices have shown that drivers are less likely to later recognize objects that they did look at when using a cell phone while driving (Strayer & Drews, 2007). These findings demonstrate that cognitive distractions such as cell phone conversations can produce inattentional blindness, or a lack of awareness of what is right before your eyes (see also, Simons & Chabris, 1999). Sadly, although we all like to think that we can multitask while driving, in fact the percentage of people who can truly perform cognitive tasks without impairing their driving performance is estimated to be about 2% (Watson & Strayer, 2010).

It may be useful to think of attention as a mental resource, one that is needed to focus on and fully process important information, especially when there is a lot of distracting “noise” threatening to obscure the message. Our selective attention system allows us to find or track an object or conversation in the midst of distractions. Whether the selection process occurs early or late in the analysis of those events has been the focus of considerable research, and in fact how selection occurs may very well depend on the specific conditions. With respect to divided attention, in general we can only perform one cognitively demanding task at a time, and we may not even be aware of unattended events even though they might seem too obvious to miss (check out some examples in the Outside Resources below). This type of inattention blindness can occur even in well-learned tasks, such as driving while talking on a cell phone. Understanding how attention works is clearly important, even for our everyday lives.

_________________________________________________________________________________________________________________________________________________________ Bargh, J., & Morsella, E. (2008). The unconscious mind. Perspectives on Psychological Science, 3 (1), 73–79.

Beilock, S. L., & Carr, T. H. (2001). On the fragility of skilled performance: What governs choking under pressure? Journal of Experimental Psychology: General, 130 , 701–725.

Broadbent, D. A. (1958). Perception and communication . London, England: Pergamon Press.

Cheesman, J., & Merikle, P. (1986). Distinguishing conscious from unconscious perceptual processes. Canadian Journal of Psychology, 40 , 343–367.

Cheesman, J., & Merikle, P. (1984). Priming with and without awareness. Perception and Psychophysics, 36 , 387– 395.

Cherry, E. C. (1953). Experiments on the recognition of speech with one and two ears. Journal of the Acoustical Society of America, 25 , 975–979.

Deutsch, J. A., & Deutsch, D. (1963). Attention: some theoretical considerations. Psychological Review, 70 , 80–90. Greenwald, A. G. (1992). New Look 3: Unconscious cognition reclaimed. American Psychologist, 47 , 766–779.

Hirst, W. C., Neisser, U., & Spelke, E. S. (1978). Divided attention. Human Nature, 1 , 54–61.

James, W. (1983). The principles of psychology . Cambridge, MA: Harvard University Press. (Original work published 1890)

Johnston, W. A., & Heinz, S. P. (1978). Flexibility and capacity demands of attention. Journal of Experimental Psychology: General, 107 , 420–435.

Merikle, P. (2000). Subliminal perception. In A. E. Kazdin (Ed.), Encyclopedia of psychology (Vol. 7, pp. 497–499).

New York, NY: Oxford University Press.

Monsell, S. (2003). Task switching. Trends in Cognitive Science, 7 (3), 134–140.

Moray, N. (1959). Attention in dichotic listening: Affective cues and the influence of instructions. Quarterly Journal of Experimental Psychology, 11 , 56–60.

Neisser, U. (1979). The control of information pickup in selective looking. In A. D. Pick (Ed.), Perception and its development: A tribute to Eleanor J. Gibson (pp. 201–219). Hillsdale, NJ: Lawrence Erlbaum Associates.

Simons, D. J., & Chabris, C. F. (1999). Gorillas in our midst: Sustained inattentional blindness for dynamic events. Perception, 28 , 1059–1074.

Spelke, E. S., Hirst, W. C., & Neisser, U. (1976). Skills of divided attention. Cognition, 4 , 215–250.

Strayer, D. L., & Drews, F. A. (2007). Cell-phone induced inattention blindness. Current Directions in Psychological Science, 16 , 128–131.

Strayer, D. L., & Johnston, W. A. (2001). Driven to distraction: Dual-task studies of simulated driving and conversing on a cellular telephone. Psychological Science, 12 , 462–466.

Strayer, D. L., Watson, J. M., & Drews, F. A. (2011) Cognitive distraction while multitasking in the automobile. In Brian Ross (Ed.), The Psychology of Learning and Motivation (Vol. 54, pp. 29–58). Burlington, VT: Academic Press.

Treisman, A. (1960). Contextual cues in selective listening. Quarterly Journal of Experimental Psychology, 12 , 242– 248.

Watson, J. M., & Strayer, D. L. (2010). Supertaskers: Profiles in extraordinary multitasking ability. Psychonomic Bulletin & Review, 17 , 479–485.

ESSENTIALS OF COGNITIVE PSYCHOLOGY Copyright © 2023 by Christopher Klein is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Cognitive Psychology: Experiments & Examples

Cognitive psychology reveals, for example, insights into how we think, reason, learn, remember, produce language and even how illogical our brains are.

Fifty years ago there was a revolution in cognitive psychology which changed the way we think about the mind.

The ‘cognitive revolution’ inspired cognitive psychologists to start thinking of the mind as a kind of organic computer, rather than as an impenetrable black box which would never be understood.

This metaphor has motivated cognitive psychology to investigate the software central to our everyday functioning, opening the way to insights into how we think, reason, learn, remember and produce language.

Here are 10 classic examples of cognitive psychology studies that have helped reveal how thinking works.

1. Cognitive psychology reveals how experts think

Without experts the human race would be sunk.

But what is it about how experts think which lets them achieve breakthroughs which we can all enjoy?

The answer is in how experts think about problems, compared with novices, cognitive psychology reveals.

That’s what Chi et al. (1981) found when they compared how experts and novices represented physics problems.

Novices tended to get stuck thinking about the surface details of the problem whereas experts saw the underlying principles that were operating.

It was partly this deeper, abstract way of approaching problems that made the experts more successful.

2. Short-term memory lasts 15-30 seconds

Short-term memory is a lot shorter than many think, cognitive psychologists find.

In fact it lasts about 15-30 seconds.

We know that because of a classic cognitive psychology study carried out by Lloyd and Margaret Peterson ( Peterson & Peterson, 1959 ).

Participants had to try and remember and recall three-letter strings, like FZX.

When tested, after 3 seconds they could recall 80 percent of them, after 18 seconds, though, they could only remember 10 percent.

That’s how short-term short-term memory is.

3. Cognitive psychology finds people are not logical

People find formal logic extremely difficult to cope with–that’s normal, cognitive psychology finds.

Here’s a quick test for you, and don’t be surprised if your brain overheats:

“You are shown a set of four cards placed on a table, each of which has a number on one side and a coloured patch on the other side. The visible faces of the cards show 3, 8, red and brown. Which card(s) must you turn over in order to test the truth of the proposition that if a card shows an even number on one face, then its opposite face is red?”

The answer is you have to turn over the ‘8’ and the brown card (for an explanation search for “Wason selection task” — even after hearing it, many people still can’t believe this is the correct answer).

If you got it right, then you’re in the minority (or you’ve seen the test before!).

When Wason conducted this classic experiment, less than 10 percent of people got it right (Wason, 1968).

Cognitive psychology finds that our brains are not set up for this kind of formal logic.

4. Example: framing in cognitive psychology

The way you frame a problem, argument or statement can have huge effects on how people perceive it.

For example, think about risk for a moment and the fact that people don’t like to take chances.

They dislike taking chances so much that even the whiff of negativity is enough to send people running for the hills.

That’s what cognitive psychologists Kahneman and Tversky (1981) demonstrated when they asked participants to imagine 600 people were affected by a deadly disease.

There was, they were told, a treatment, but it is risky.

If you decided to use the treatment, here are the odds:

“A 33% chance of saving all 600 people, 66% possibility of saving no one.”

When told this, 72 percent of people thought it was a good bet.

But, when presented the problem this way:

“A 33% chance that no people will die, 66% probability that all 600 will die.”

…the number choosing it dropped to 22 percent.

The beauty of the study is that the outcomes are identical, it’s just the framing that’s different.

Cognitive psychology shows that the way we think is heavily influenced by the terms in which issues are expressed.

5. Attention is like a spotlight

We actually have two sets of eyes — one set real and one virtual, cognitive psychology finds.

We have the real eyes moving around in their sockets, but we also have ‘virtual eyes’ looking around our field of vision, choosing what we pay attention to.

People are using their virtual eyes all the time: for example, when they watch each other using their peripheral vision.

You don’t need to look directly at an attractive stranger to eye them up, you can look ‘out of the corner of your eye’.

Cognitive psychologists have called this the ‘spotlight of attention’ and studies have actually measured its movement.

It means we can notice things in the fraction of a second before our eyes have a chance to reorient.

→ Read on: The Attentional Spotlight

6. The cocktail party effect in cognitive psychology

It’s not just vision which has a kind of spotlight, our hearing is also finely tuned, cognitive psychologists have discovered.

It’s like when you’re at a cocktail party and you can tune out all the voices, except the person you’re talking to.

Or, you can tune out the person you’re talking to and eavesdrop on a more interesting conversation behind.

A beautiful cognitive psychology demonstration of this was carried out in the 1950s by Cherry (1953) .

He found that people could even distinguish the same voice reading two different messages at the same time.

→ Read on: The Cocktail Party Effect

7. Children’s cognitive psychology example

If you take a toy duck and show it to a 12-month-old infant, then put your hand under a cushion, leave the duck there and bring your hand out, the child will only look in your hand, almost never under the cushion.

At this age, children behave as though things they can’t see don’t even exist.

As the famous child psychologist Jean Piaget noted:

“The child’s universe is still only a totality of pictures emerging from nothingness at the moment of action, to return to nothingness at the moment when the action is finished.”

And yet, just six months later, a child will typically look under the cushion, studies in cognitive psychology have found.

It has learnt that things that are hidden from view can continue to exist — this is known as object permanence .

This is just one miracle amongst many in developmental  psychology and cognitive psychology.

8. The McGurk effect in cognitive psychology

The brain is integrating information from all our senses to produce our experience, cognitive psychology shows.

This is brilliantly revealed by the McGurk effect ( McGurk & MacDonald, 1976 ).

Watch the following clip from a BBC documentary to see the effect in full.

You won’t believe it until you see and hear it yourself.

The sensation is quite odd:

9. Implanting false memories

People sometimes think of their memories as being laid down, then later either recalled or forgotten, with little change in the memories themselves between the two.

In fact, cognitive psychology shows that the reality is much more complex and, in some cases, alarming.

One of the most dramatic examples of these studies demonstrated that memories can be changed, or even implanted later, was carried out by Elizabeth Loftus.

In her study, a childhood memory of being lost in a mall was successfully implanted in some people’s mind, despite their families confirming nothing like it had ever happened to them.

Later research in cognitive psychology have found that 50 percent of participants could have a false memory successfully implanted.

→ Read on: Implanting False Memories

10. Why the incompetent don’t know they’re incompetent

There all kinds of cognitive biases operating in the mind, cognitive psychology has found.

The Dunning-Kruger effect , though, is a favourite because it explains why incompetent people don’t know they’re incompetent.

David Dunning and Justin Kruger found in their studies that people who are the most incompetent are the least aware of their own incompetence.

At the other end of the scale, the most competent are most aware of their own shortcomings.

→ Explore more: Cognitive Biases : Why We Make Irrational Decisions

Author: Dr Jeremy Dean

Psychologist, Jeremy Dean, PhD is the founder and author of PsyBlog. He holds a doctorate in psychology from University College London and two other advanced degrees in psychology. He has been writing about scientific research on PsyBlog since 2004. View all posts by Dr Jeremy Dean

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Course info.

• Prof. John D. E. Gabrieli

Departments

• Brain and Cognitive Sciences

As Taught In

• Cognitive Science

Learning Resource Types

Introduction to psychology.

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Session Overview

Session Activities

Read the following before watching the lecture video.

• Study outline for K&R Chapter 3 (PDF)
• [Stangor] Chapter 4, “Sensing and Perceiving”

Lecture Videos

View Full Video Lecture 7: Attention View by Chapter What Do We Mean by “Attention”? Demonstrations of the Limits of Attention Hypnosis and the Stroop Effect Visual Search and Attentional Blink Object Tracking and Improving Attention Subliminal Perception Video Resources Removed Clips Lecture Slides (PDF)

Discussion: Attention

Today we’re also going to talk about attention. How we engage with the world, but only a small part of it at a time. Why we can’t engage with the whole thing, and what it would be like to engage with the whole thing. Limits on our attention: why we can’t perceive everything at once…. Read more »

Check Yourself

Describe an example of selective attention. Does selective attention completely block out all other sensory input? If not give an example. What are the advantages and disadvantages of processes involved in selective attention?

› Sample Answer

One example of selective attention is the ability to focus on one person speaking in a crowd of people speaking. Selective attention does not completely block out all other sensory input. For example, the cocktail party phenomenon occurs when we are only attending to and aware of one person speaking. We seem to have no attention to the surrounding conversations. However, if someone says your name or something related to you, you quickly divert our attention to the person that said your name. Therefore, while consciously unaware of the surrounding sensory input, our brains are constantly monitory surrounding input.

The advantage of selective attention is that it allows us to focus on important elements of our environment while blocking out things that could be distracting. This is especially important for survival in dangerous situations. However, focusing too much on one thing could be a disadvantage if other things in the environment require more urgent focus. The “cocktail party phenomenon” is a process that allows us to monitor the environment while still focusing our attention on important things.

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Attention (Psychology Theories)

One of the primary topics in cognitive psychology is Attention. You might be here because you're studying for a test or writing a research paper on selective attention theories. Either way, we strive to give you the best overall information on the topic so you can continue your studies and contributions to psychology. 

Reading this page, there are many inputs to your brain:

• Information presented in front of your eyes on the screen
• The feeling of your feet on the floor
• Sounds in your ears from ambient noise
• The feeling of your fingers on the mouse or phone

When it comes to attention, you can't focus on all of these at once. However, you can focus on one for a certain period. 

What is Attention in Psychology?

Psychology defines attention as concentrating our consciousness on certain sensory inputs or processes. It includes our ability to focus on information relevant to a task at hand while ignoring other useless information. 

Many psychologists have studied and created theories regarding attention. On this page, we will briefly go over some of these theories. More detailed information about these theories can be found on our website. Click around and pay  attention  to what you're reading about attention! 

Some important theories and phenomena to know regarding attention include: 

• Broadbent's Filter Model: A model suggesting that individuals have limited attentional resources, filtering out stimuli based on physical characteristics. 
• Treisman's Attenuation Model: Proposes that instead of filtering out information thoroughly, we lower the volume on unattended stimuli, thus "attenuating" them.
• Change Blindness : The failure to notice significant changes in a visual scene.
• Inattention Blindness : The inability to notice something fully visible because attention is directed elsewhere.
• Subliminal Advertising : Advertising messages presented below the threshold of conscious awareness.
• The Stroop Effect : A cognitive interference where the brain struggles to differentiate the color of a word from the word's text.
• Multitasking : Engaging in multiple tasks simultaneously often reduces the effectiveness of each task.
• Attention-Deficit/Hyperactivity Disorder (ADHD): A neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity.

Selective Attention Theories 

Broadbent's filter model (1958).

In one of the earliest attention models, Donald Broadbent proposed that we filtered out information based on physical characteristics. He said we had a filter to pick what to listen to. For example, if you listen to your sister with a higher-pitched voice, your attention would experience a "bottleneck," a term used to describe a point in the process where the flow of information gets restricted or limited. As a result of this bottlenecking, you wouldn't hear your brother speaking in the background.

Broadbent's filter model, including the Cocktail Party Effect, has a few holes. Imagine yourself at a party and deep conversing with a beautiful woman until a guy halfway across the room says your name. Somehow, you hear your name. This means you didn't "bottleneck" your attention to just the beautiful woman...

Treisman's Attenuation Model (1964)

Anne Treisman was one of Broadbent's students and continued his work on attention theory. She theorized that instead of "bottlenecking" what information passed to our attention, we just "attenuated" it. Think of this like a volume knob, where we can turn down and turn up certain stimuli.

When talking to your sister, you turn down everything else so you can listen attentively. When someone else says your name at a party, the volume is low but low enough that you still hear it, and then you turn the volume up because your name is an important word to you. 

These two theories are just a few selective attention theories . On our page, you will be able to learn about many more theories and phenomena regarding selective attention, including: 

• The Cocktail Party Effect (why you pay attention to your name when at a cocktail party or other noisy event) 
• A Dichotic Listening Task
• Pertinence Model of Attention
• Norman’s Pertinence Model
• Johnston and Heinz's Multimode Theory

Inattentional and Change Blindness

• Inattentional Blindness

Another very similar effect is called Inattentional Blindness. This is a person's failure to notice something fully visible because their attention was focused on something else. For example, when you look down at your phone while driving, you might not notice a deer trying to cross or another car changing lanes. 

Examples of Inattentional Blindness: The Invisible Gorilla

One of the most powerful examples of inattentional blindness is the " Invisible Gorilla " study. This website has a whole page dedicated to the impact and significance of this study. For now, here's just some brief information about how the study was conducted: 

"In 1999, Chris Chabris and Dan Simons conducted an experiment called the “Invisible Gorilla Experiment.” They told participants they would watch a video of people passing around basketballs. In the middle of the video, a person in a gorilla suit walked through the circle momentarily. 

The researchers asked participants if they had seen the gorilla. Of course, they would, right? Not so fast. Before the researchers asked participants to watch the video, they asked them to count how many times people in the white shirts passed the basketball.  In this initial experiment, 50% of the participants failed to see the gorilla!" 

Relevance and Significance

The "Invisible Gorilla" experiment is more than just a quirky trick on the mind. It underscores a fundamental aspect of human cognition: our attention is limited, even when focused intently on a task. It reveals the boundaries of human perception and illustrates how easy it is for us to miss even glaringly obvious events when our attention is directed elsewhere. This has broader implications in driving safety, aviation, and everyday tasks. It reminds us that our perception of the world isn't always as complete or accurate as we believe and that there are limitations to our conscious awareness.

Change Blindness

Change Blindness is a phenomenon that occurs when a participant is shown two different stimuli but doesn't notice any changes. Why is this important? It's used as evidence against eyewitness testimonies and situations like distracted driving.

Change blindness is slightly different from inattentional blindness. Here's what the American Psychological Association has to say about the difference: 

"Inattentional blindness is one of two perceptual phenomena that have begun to change scientists' view of visual perception, from one of a videotape to something far less precise. Beginning in the 1970s, researchers began to recognize a phenomenon called "change blindness," finding that people often fail to detect change in their visual field as long as the change occurs during an eye movement or when people's view is otherwise interrupted. Such findings have spurred debates about how--and indeed, whether--the brain stores and integrates visual information." 

Examples of Change Blindness: Continuity Errors

Change blindness often prohibits us from noticing errors in movies and TV shows! The following examples from our change blindness post show how much we can miss! How many of these errors have you noticed? 

• "In the movie  New Moon , Jacob has a new tattoo. But the location of that tattoo is rarely consistent throughout the movie! It appears on the top of his arm, and then lower down his arm in other shots. 
• In  Blade Runner , Zhora’s stunt double is shown. A lot. With some pretty obvious close-up shots. 
• Also in  Blade Runner , Roy Batty dies in a storm at night. Shortly after, a pair of doves is released – to a cloudless, beautiful sunny day. 
• In one of the biggest scenes of  Ace Ventura: When Nature Calls , we watch Jim Carrey solve a mystery as Vincent Cadby plays chess next to him. In a later shot, all of the chess pieces are gone! Even later, the chess pieces come back!"

Notable Phenomena in The Study of Attention

Subliminal advertising.

Regarding attention in psychology, subliminal advertising is one of the most interesting topics. Is it a real thing? Does it increase profits? 

There are many studies on subliminal advertising, but the conclusion is simple: they don't work. Informal and formal studies on subliminal advertising show that the attempts to encourage an audience's behavior through subliminal advertising have no significant effect on their behavior. That doesn't stop brands from using it in their ads! 

This blog post from HubSpot lists a few ways brands add "subtle" messages to their advertisements and logos. Whether or not these moves work to influence customers is up in the air, but they are certainly great examples of design and creativity! 

The Stroop Effect

The Stroop Effect is a compelling phenomenon when the brain shows decreased reaction time while focusing on two stimuli. 

The best way to explain the Stroop effect and how it relates to attention is to have you attempt to read the colors below. Don't focus on reading the words, instead, say the color of each word aloud. 

It's quite difficult.

There are many theories for why this happens:

• Speed of Processing Theory
• Parallel Distributed Processing
• Automaticity theory 

Read all about these theories and variations on The Stroop Effect right here !

• Multitasking

One of the practical applications of studying attention is our understanding of how multitasking works. When most people think of multitasking, they imagine themselves effectively juggling several tasks simultaneously. However, in reality, what they're often doing is not genuine simultaneous multitasking but rather "task-switching." This is a rapid shifting of attention from one task to another and back again.

In almost all of the studies I've looked at, what is typically termed multitasking greatly decreases the effectiveness of both tasks. In other words, what many perceive as multitasking seems to be a burden more than a productivity trick.

Why? Cognitive psychologists attribute this decrease in performance to the inherent "switch costs" of task-switching. When your brain transitions from one complex task to another, it requires time and cognitive resources to "switch gears." Even if these switches seem instantaneous, they add up and significantly detract from our overall efficiency. This indicates that attention doesn't prefer to be divided, and a concentrated focus for extended periods is more productive than perpetually switching attention between tasks.

The American Psychological Association shares this idea more eloquently:

“[A]lthough switch costs may be relatively small, sometimes just a few tenths of a second per switch, they can add up to large amounts when people switch repeatedly back and forth between tasks. Thus, multitasking may seem efficient on the surface but may take more time and involve more error. Meyer has said that even brief mental blocks created by shifting between tasks can cost as much as 40 percent of someone’s productive time.”

There are exceptions, of course. Some simpler, automated tasks might be performed simultaneously without detriment. For instance, walking while talking. But for tasks requiring deeper cognitive processing, it's clear that "multitasking" is often detrimental. This has led me to believe that the popular notion of multitasking as a productivity tool is largely a myth . Though short bursts might feel productive, you might benefit more from mindfully batching tasks or focusing wholeheartedly on one task at a time.

Attention-Deficit/Hyperactivity Disorder (ADHD)

Paying attention for long periods can be tough in a world requiring us to multitask constantly. Notifications take our minds away from the tasks at hand. The world's problems, the problems in our homes, and other issues may keep us occupied when we're trying to work. Is our inability to pay attention a result of our environment, or the sign of a larger issue encoded in our DNA? 

The answer may surprise you if you have been struggling to pay attention. A Reddit user asked users on the ADHD subreddit how they knew they had ADHD as an adult . Here's one response: 

"Female, 25, did very good in school up until age 15. Then it started getting somewhat harder, but with minimal effort I could still make it. In uni I used caffeïne to study. Struggled all the way through until my graduation project from my tech U master. It was too much (along with other things in my personal life) and I broke down.

I have always felt like there was something off, like a blockage in my head as you describe. Also, I never felt really energized, unless it was after a long vacation with my parents in my childhood. I was dreamy, worried, felt emotions very strong and switched between them very quickly.

After my breakdown I researched my symptoms (no concentration, tired, irritable, etc) and came to ADHD. Went to the doctor with that, got a reference to a psychologist, got tested and here we are. Now I have therapy and I have to deal with being overworked, depression and (performance) anxiety due to 'untreated' (it can't be cured) ADHD." 

The inability to maintain focus or attention may be a sign of neurodivergence. 

Free ADHD Test

Often, people take ADHD tests to determine whether they need to see a medical professional. Experts believe 2-4% of adults struggle with ADHD, although diagnosis for ADHD can be difficult. Men and women often display different symptoms of ADHD, which may differ as a person ages. 

Although an ADHD test is available on this website, it is no longer a replacement for a diagnosis. Reach out to a medical professional if you believe you are struggling with ADHD or another type of neurodivergence. 

There's a lot to learn about attention and psychology! Keep reading to discover how our minds pay attention to the world around us.

Related posts:

• What is ADHD? (Free ADHD Test + Symptoms)
• Inattentional Blindness (Definition + Examples)
• Change Blindness (Definition + Examples)
• The Invisible Gorilla (Inattentional Blindness)
• The Psychology of Long Distance Relationships

Reference this article:

About The Author

• Selective Attention Theories
• Invisible Gorilla Experiment
• Cocktail Party Effect
• Stroop Effect

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How We Use Selective Attention to Filter Information and Focus

Verywell / Emily Roberts 

How Selective Visual Attention Works

How selective auditory attention works, examples of selective attention, theories of selective attention, factors that influence selective attention.

Selective attention is the process of focusing on a particular object in the environment for a certain period of time. Attention is a limited resource, so selective attention allows us to tune out unimportant details and focus on what matters. This differs from inattentional blindness , which is when you focus hard on one thing and fail to notice unexpected things entering your visual field.

At a Glance

The world is full of stimuli competing for our attention, but we can only focus on so much at a time. That's why we rely on selective attention to help us attend to important things in the environment. Some experts have likened it to a spotlight or zoom lens, essentially highlighting information that is particularly relevant in any given moment.

How Does Selective Attention Work?

At any given moment, we are subjected to a constant barrage of sensory information. The blare of a car horn from the street outside, the chatter of your friends, the click of the keys as you type a paper for school, the hum of the heater as it keeps your room warm on a brisk autumn day.

But in most cases, we don't pay attention to each and every one of these sensory experiences . Instead, we center our attention on certain important elements of our environment while other things blend into the background or pass us by completely unnoticed. So how exactly do we decide what to pay attention to and what to ignore?

Imagine that you are at a party for a friend hosted at a bustling restaurant. Multiple conversations, the clinking of plates and forks, and many other sounds compete for your attention. Out of all these noises, you find yourself able to tune out the irrelevant sounds and focus on the amusing story that your dining partner shares.

How do you manage to ignore certain stimuli and concentrate on just one aspect of your environment? This is an example of selective attention. Because our ability to attend to the things around us is limited in terms of both capacity and duration, we have to be picky about the things we pay attention to.

Attention acts somewhat like a spotlight, highlighting the details that we need to focus on and casting irrelevant information to the sidelines of our perception.

To focus and sustain our attention, we have to filter out the things that aren't relevant to us in any give moment. That means we focus on some things, while simultaneously ignoring others. Attention is a limited resource, which means we have to use the resources we have available to focus on the events that are the most important.

There are two major models describing how visual attention works.

Spotlight Model

The "spotlight" model works much as it sounds—it proposes that visual attention works similar to that of a spotlight. Psychologist William James suggested that this spotlight includes:

• Focal point : The center of focus is known as the focal point. In this area, things are viewed clearly.
• Fringe : The area surrounding this focal point, known as the fringe, is still visible but not clearly seen.
• Margin : Finally, the area outside of the fringe area of the spotlight is known as the margin.

Zoom-Lens Model

The second approach is known as the "zoom-lens" model. While it contains all the same elements of the spotlight model, it also suggests that we are able to increase or decrease the size of our focus much like the zoom lens of a camera.

However, a larger focus area also results in slower-processing since it includes more information so the limited attentional resources must be distributed over a larger area.

Some of the best-known experiments on auditory attention are those performed by psychologist Colin Cherry. Cherry investigated how people are able to track certain conversations while tuning others out, a phenomenon he referred to as the "cocktail party" effect.

In these experiments, two auditory messages were presented simultaneously with one presented to each ear. Cherry then asked participants to pay attention to a particular message, and then repeat back what they had heard.

He discovered that the participants were able to easily pay attention to one message and repeat it, but when they were asked about the contents of the other message, they were unable to say anything about it.

Cherry found that when contents of the unattended message were suddenly switched (such as changing from English to German mid-message or suddenly playing backward) very few of the participants even noticed. Interestingly, if the speaker of the unattended message switched from male to female (or vice versa) or if the message was swapped with a 400-Hz tone, the participants always noticed the change.

Cherry's findings have been demonstrated in additional experiments. Other researchers have obtained similar results with messages including lists of words and musical melodies.

Some examples of how you might use selective attention each day include:

• Listening to what your friend is saying when you are in a noisy room
• Tuning out the sound of a TV in the background while you are reading a book
• Listening to what a presenter is saying even though other noises and distractions are competing for attention
• Focusing on the road while driving, even though there are other sights and sounds that might distract you

In some cases, you might become so focused on a particular stimulus that you overlook when other things happen. Inattentional blindness , for example, occurs when we are so focused on one thing that we don't notice something entering our visual field. Similarly, change blindness occurs when we are selectively attending to one aspect of a scene and don't notice other changes that may occur.

Theories of selective attention tend to focus on when stimulus information is attended to, either early in the process or late.

Broadbent's Filter Model

One of the earliest theories of attention was Donald Broadbent's filter model. Building on the research conducted by Cherry, Broadbent used an information-processing metaphor to describe human attention.

Broadbent suggested that our capacity to process information is limited in terms of capacity, and our selection of information to process takes place early on in the perceptual process .

To do this, we utilize a filter to determine which information to attend to. All stimuli are first processed based on physical properties, including color, loudness, direction, and pitch. Our selective filters then allow certain stimuli to pass through for further processing while other stimuli are rejected.

Treisman's Attenuation Theory

Treisman suggested that while Broadbent's basic approach was correct, it failed to account for the fact that people can still process the meaning of attended messages. Treisman proposed that instead of a filter, attention works by utilizing an attenuator that identifies a stimulus based on physical properties or by meaning.  

Think of the attenuator like a volume control—you can turn down the volume of other sources of information in order to attend to a single source of information. The "volume" or intensity of those other stimuli  might be low, but they are still present.

In experiments, Treisman demonstrated that participants were still able to identify the contents of an unattended message, indicating that they were able to process the meaning of both the attended and unattended messages.

Memory Selection Models

Other researchers also believed that Broadbent's model was insufficient and that attention was not based solely on a stimulus's physical properties. The cocktail party effect serves as a prime example. Imagine that you are at a party and paying attention to the conversation among your group of friends.

Suddenly, you hear your name mentioned by a group of people nearby. Even though you were not attending to that conversation, a previously unattended stimulus immediately grabbed your attention based on meaning rather than physical properties.  

According to the memory selection theory of attention, both attended and unattended messages pass through the initial filter and are then sorted at a second-stage based upon the actual meaning of the message's contents.

Information that we attend to based upon meaning is then passed into short-term memory .

Resource Theories of Selective Attention

More recent theories tend to focus on the idea of attention being a limited resource and how those resources are divvied up among competing sources of information. Such theories propose that we have a fixed amount of attention available and that we must then choose how we allocate our available attentional reserves among multiple tasks or events.

However, critics note that attentional-resources theories are broad, vague, and don't fully explain all aspects of attention.

While no single theory serves as an all-encompassing explanation, each does seem to explain different aspects of attention.

Ecological View of Attention

More recently, researchers have proposed an ecological view of attention that focuses on an organism's interactions with its environment rather than on the limitations of brain resources. This view suggests that attention is selectively focused because it ensures that behaviors are properly oriented to the environment an organism's goals.

Several factors can influence selective attention. The location from where the sound originates can play a role. For example, you are probably more likely to pay attention to a conversation right next to you rather than one several feet away.

For example, presenting messages to different ears will not lead to selecting one message over the other. The two messages must have some sort of non-overlap in time for one to be selectively attended to over the other. As mentioned previously, changes in pitch can also play a role in selectivity.

The number of auditory selections that must be tuned out to attend to one can make the process more difficult. Imagine that you are in a crowded room, and many different conversations are taking place all around you.

Selectively attending to just one of those auditory signals can be very difficult, even if the conversation is taking place nearby.

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Laberge DL. Attention.  Psychological Science . 1990;1(3):156-162. doi:10.1111/j.1467-9280.1990.tb00188.x.

Schad DJ, Engbert R. The zoom lens of attention: Simulating shuffled versus normal text reading using the SWIFT model .  Vis cogn . 2012;20(4-5):391–421. doi:10.1080/13506285.2012.670143

Cherry EC. Some experiments on the recognition of speech, with one and with two ears . The Journal of the Acoustical Society of America. 1953;25(5):975-979. doi:10.1121/1.1907229.

Broadbent DE.  Perception and Communication . London: Pergamon Press; 1958.

Treisman AM. Selective attention in man .  British Medical Bulletin . 1964;20(1):12-16. doi:10.1093/oxfordjournals.bmb.a070274.

Lachter J, Forster KI, Ruthruff E. Forty-five years after Broadbent (1958): Still no identification without attention .  Psychological Review . 2004;111(4):880-913. doi:10.1037/0033-295x.111.4.880.

Sternberg RJ, Sternberg K, Mio JS.  Cognitive Psychology . Belmont, CA: Wadsworth/Cengage Learning; 2012.

Lev-Ari T, Beeri H, Gutfreund Y. The ecological view of selective attention .  Front Integr Neurosci . 2022;16:856207. doi:10.3389/fnint.2022.856207

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

• EEG Methods
• Neurodiversity

Many Ways to Measure Attention

Attention is a multifaceted, multisensory cognitive phenomenon that can be studied in many ways.  Here we describe a few methods used in the lab to measure its different aspects.

Attention is a fundamental aspect of cognitive function.  Yet it is not a single thing but rather a multifaceted concept that includes dividing, switching, selecting, orienting, searching and sustaining. To explore these many facets of attention, numerous methodological approaches have evolved.  Here we outline 5 common types of tasks which are used to measure attention in the lab that can also be combined with neuroimaging techniques such as EEG.

Tasks for measuring attentional orienting.

One of the most classic attention tasks is the spatial cueing task, sometimes called the “Posner” task . It measures how fast people can orient or shift their attention to particular locations in space in response to a cue.  The task involves the participant looking at a screen with a square in the center to the right or left of which an image will occur.  An arrow cues the participant which direction the image will occur.  The participant must press a key as soon as the image appears in the cued location.  Essentially this task measures how quickly we shift attention from the cue to the target when alerted. Often in some trials an invalid cue is presented (i.e. pointing the wrong direction) or the cue is omitted to see how this influences the reaction time.

Deficits in performance occur in some clinical disorders (e.g. ADHD ) and become more evident with age. Multiple variants of the task exist using EEG. For example, studies from John Foxe’s lab have explored the role of occipital alpha activity in the anticipatory phase between cue and target in both the visual and auditory domain, whilst studies from Anna Nobre’s lab have demonstrated the role of alpha activity when participants orient their attention within a remembered spatial array (e.g. stored in their working memory).

2. Tasks for measuring attentional control.

The attention network test (ANT) , developed by Jin Fan (task can be downloaded here for research purposes), is a more recent paradigm that combines Posner’s cued reaction time task and Eriksen’s flanker task to measure three types of attentional control – alerting, orienting and executive (or decision making).  In addition to the standard Posner task, the test involves presentation of the cue arrow presentation flanked by either congruent (same direction) or incongruent arrows (opposite direction) which requires the participant to use more sophisticated (executive) decision making to determine which way the arrow is pointing. This 3-in-1 approach has been used to assess attentional performance in a wide number of clinical populations including autism , schizophrenia , anxiety and ADHD , and in combination with EEG to demonstrate age-related decline and other brain changes.

Schematic of the ANT. A fixation cross appears in the center of the screen all of the time. In each trial, depending on the cue condition (no cue, center cue, or spatial cue), a cue may appear for 200 ms. After a variable duration (300–1450 ms), the target (the center arrow) and flankers of two left and two right arrows (congruent or incongruent flankers) are presented. The participant makes a response to the target’s direction within a time window of 2000 ms. The target and flankers disappear after the response is made. The target and post-target fixation period lasts for a variable duration (3000–4200 ms). From Fan et al 2007 .

3. Tasks for measuring sustained attention and vigilance.

There are a number of different vigilance tasks where participants have to sustain their attention over time, typically towards a continuous sequence of stimuli. These includes tasks such as the  sustained attention to response task (SART ) and Conners continuous performance test (CPT), where participants are presented a continuous series of letters or images in a fixed or random fashion and have to press a spacebar whenever they see an X.  This requires both being alert to the X and withholding response to other letters.  The results have been shown to be sensitive to various clinical disorders (e.g. ADHD ) and experimental interventions (e.g. caffeine administration ). Other tasks which require sustained attention include the rapid serial visual presentation (RSVP) task, the psychomotor vigilance task (PVT) and the oddball task , where participants have to respond to a rare or deviant target. For example, the  rapid serial visual presentation (RSVP) task can be adapted to measure the attentional blink phenomenon when two targets are presented in close succession.

Schematic of the Sustained Attention to Response Task (SART), demonstrating the sequence of events and timings for the SART. Figure depicts (A) a go trial (requiring a response to the presentation of the go-digit 1), and (B) a no-go trial (requiring the withholding of a response to the no-go digit 3). In the Fixed version of the SART, the digits 1–9 are presented within a fixed sequence that is repeated 25 times. In the Random version of the SART, the digits are presented in a pseudo-random order. All participants respond on the response cue. Taken from Johnson et al 2007

4. Task for measuring visual search.

  Visual search uses a range of stimulus types from single letters through to complex scenes (see here for a large visual search dataset from Jeremy Wolfe lab ). In both cases, participants have to search the array or scene for the target stimulus, keeping in mind what they are looking for and ignoring irrelevant distractors. There are various theories about how visual search best operates from the perspective of the brain and EEG studies can help to provide insights into the factors which influence searching performance .

(from http://search.bwh.harvard.edu/new/index.html)

• Measuring attentional biases to emotion.

  The dot probe task allows researchers to measure the way attention is biased by emotional stimuli (e.g. by threatening information). The classical version of the task is a covert attention task with words or faces (a word version of the task can be downloaded here for research purposes), and allows the researcher to measure how rapidly the emotionally charged information “grabs” the participant’s attention. Strong biases to threatening or negative stimuli are often observed in patients with anxiety , however the reliability of the task is not always consistent and is a reminder that just because a task is used frequently, doesn’t necessarily mean it is always the best task to use.

A schematic of the dot probe task. On the left is a congruent trial where the dots are presented in the location of the emotional stimulus, whilst on the right is an incongruent trial where the dots are presented in the location of the neutral stimulus.

In summary, attention is a multifaceted phenomenon and there are a range of tasks to measure these various facets.  While we have presented a few that are primarily visual in nature, attention also extends to other sensory modalities and may indeed be different across modalities.  A person may, for example, find directing visual attention easy but auditory attention more difficult.  Furthermore, not all methods are discussed here – there are a host of others as well, for example, relating to divided attention (e.g. between two modalities), attentional interference (e.g. stroop tasks) or attentional flexibility.  Lastly,  we point out that these tasks are simple in nature and how they relate to more complex paradigms is not clear.  Indeed, there is no single test to truly assess the strengths and weaknesses in someone’s attentional functioning.  Rather it would require a battery of tasks of increasing complexity that integrate different facets of attention and sensory modalities.

One thought on “ Many Ways to Measure Attention ”

These are all traditional and well-documented experimental paradigms. But I would argue that the question of construct validity – whether what the tasks aim to measure actually maps on to a parameter of individual differences that is meaningful in the real world – is relatively unproven. See here https://www.sites.google.com/site/samwass/blog-for-researchers/untitledpost for a discussion of this – and see here https://www.sites.google.com/site/samwass/blog-for-researchers/onyerkes-dodsonandnaturalisticattention for a discussion of more naturalistic, ecologically valid approaches to measuring attention…

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Sridhar Venkatesh

Head, Schools for Strong Minds

Sridhar Venkatesh leads Sapien Labs Schools for Strong Minds Program and advises on the data and analytics platform that powers its global data acquisition.

Sridhar brings vast experience across the data and technology sectors along with a deep interest in mind health and mental wellbeing. His career focus has been in product management where he has successfully brought products to market in spaces including data infrastructure, AI/ML, wireless and telecom, and enterprise security. He founded two startups and led product and business teams at three others, giving him deep experience in building products and businesses from scratch. Outside of work, Sridhar is a long-distance runner and ocean swimmer. He served on the board of the American International School in Chennai for 8 years, developing a strong interest in K-12 education.

Sridhar holds a BS and MS in electrical engineering from MIT and is currently working on a PhD in Psychology from the California Institute for Human Sciences.

“When I first heard about Sapien Labs, I was incredibly impressed – both by the amazing team as well as the magnitude of the challenge. There are so many organizations focused on mental health, but Sapien Labs’ approach goes to the heart of the problem, understanding root causes and working on prevention. The other aspect I love is that at its core, Sapien Labs is a research organization. We care deeply about the science and using the findings to make an impact. I feel so grateful to be a part of this team, and hopefully our work helps millions today and in future generations.”

David Blanchflower

Fellow, Adviser

David Blanchflower is the Bruce V. Rauner Professor of Economics at Dartmouth College. His areas of research include unemployment, labour economics and well-being. He has published extensively on happiness and the relationship between the economy, social factors and wellbeing. His recent work has been on the declining mental health of the young.

He was previously member of the Monetary Policy Committee, Bank of England, and was honored as a Commander of the British Empire (CBE) for ‘services to the Monetary Policy Committee and economics’. He is a part-time professor at the University of Glasgow, UK and a research associate at the National Bureau of Economic Research. He is currently an advisor to the Human Development Report Office at the United Nations.

“As a researcher who has been interested in wellbeing and the relationship to the economy I have worked with numerous national and global survey datasets over my career. The Global Mind data has a scale, breadth and timeliness that has not been available before and is one of the richest and topical datasets I have worked with. I am excited by its enormous potential as a resource for researchers around the world to examine many of the questions I have grappled with over the years. It helps us understand how the world is now.”

Dr. Sr. John-Mary Vianney

Director, Sapien Labs Center for Research of Brain and Mind at the Nelson Mandela African Institution of Science and Technology

Dr. Sr. John-Mary Vianney is Professor of Neuroscience in the Department of Health and Biomedical Sciences at the Nelson Mandela African Institution of Science and Technology (NM-AIST). One of only a few Neuroscientists in East Africa, Dr. Vianney has dedicated her career to investigating neurodegenerative diseases and brain atrophy and factors that contribute to neuronal maintenance and regeneration. She is also passionate about teaching and has taught courses in Neurobiology, One Health, and Global Health. She has also been an active member of multiple academic and professional societies, including the Society for Neuroscience and the American Association for the Advancement of Science.

Dr. Sr John-Mary holds a Ph.D. in Biological Sciences with a specialization in Neurosciences from Western Michigan University. She is also a Catholic nun affiliated with the Sisters of Saint Therese of the Child Jesus (Theresian Sisters) based in Bukoba, Tanzania.

It has been my passion to try to understand what factors positively contribute to neuronal maintenance and regeneration, and vice versa. The mission and approach of Sapien Labs in performing a large-scale study on brain and mental wellbeing across continents will continue to reveal much about the brain interactions to its surroundings. I am excited to be a bridge between the Center for Research of Brain and Mind (CEREBRAM) at NM-AIST and Sapien Labs, as we collaborate to promote wellbeing in Tanzania and across the globe.

Jennifer Newson, Ph.D.

Lead Scientist, Cognitive and Mental Health

Jennifer came to Sapien Labs out of a curiosity for the workings of the human mind and a desire to blend academic research with real world impact. At Sapien Labs she has spearheaded the development of the MHQ and the global roll-out of the Mental Health Million Project and continues to lead data exploration and the development of novel tools.

Her previous research has spanned multiple subfields of the brain including cognitive neuroscience, olfaction science, and mental health and wellbeing where she has worked on the relationship between attention and memory, scene reconstruction in memory and imagination using fMRI, and the translation of olfaction induced insights using EEG and measures of emotional, physical and mental wellbeing into product design. Prior to Sapien Labs she was Head of Neuroscience Research at Givaudan and brings a broad perspective across academic, commercial and not-for profit organizations.

She has a Ph.D. in Experimental Psychology from Oxford University and a B.Sc. Hons in Neuroscience from the University of Edinburgh. She did her postdoctoral work at the Wellcome Center for Neuroimaging at the University College London.

“I’m interested in how scientific insights can be translated into tangible applications that have real-life benefit, and the opportunity to shape the future of brain health and mental health care, ultimately enabling people to receive care and support that is more tailored to their needs. I love the “anything’s possible” perspective at Sapien Labs which helps us to navigate ambitious initiatives/projects and to seek solutions and opportunities that go beyond the status quo. I am also excited to be able to bring together my background across neuroscience and cognitive neuroscience and my experience in translating fundamental science into technologies in the commercial sector to build something useful for the field of mental health care.”

Maya Thiagarajan

Founder and Education Director, TREE

Maya brings to Sapien Labs a perspective on ways to integrate research insights into curriculum and policy in education for better learning and wellbeing.

Maya is currently the Founder and Education Director of TREE, an organization that develops, supports, and inspires teachers in India. She has previously taught in a wide range of high schools including public school in New Hampshire and inner-city Baltimore as part of Teach for America, and in private schools in New York City, Boston and most recently at UWC Singapore. She now serves on the Board of the Kodaikanal International School. She is the author of Beyond the Tiger Mom: East-West Parenting for the Global Age, and writes widely on parenting and education.

Maya has an MA in Education Policy from Harvard University and a BA in English from Middlebury College.

As an educator, I am fascinated by the differences in how people learn and navigate the world and how this is influenced by culture, methods of teaching and life experiences. Sapien Labs’ research in the area of mental and cognitive health brings new perspectives and understanding to this and can have far-reaching implications for educators and education systems across the globe. Finding ways to enable better adolescent mental health and cognitive health in low-income communities for greater school success are also both particularly important challenges that I am excited to contribute to.

Robert Carter

Acting Executive Director and Board Member

As a Board Member at Sapien Labs Rob brings a perspective on team building and strategies to integrate research into consumer and healthcare products.

Rob is a senior executive at Avalere Health with deep healthcare relationships and a broad understanding of the behavioral health space including expert understanding of the models used by large companies and payers to address behavioral health. He has advised large private equity investors in the diligence and purchase of companies delivering substance abuse treatment; outpatient psychiatry, autism services and running inpatient care for eating and other disorders. Rob also has significant experience developing successful business models and acquiring early customers and partnerships for early-stage data/tech driven companies.

Rob has a Masters in Public and Private Management from the Yale University School of Management and a BA in Economics from Bucknell University.

As human beings we all struggle through our lifetimes to understand the elements that shape our mental trajectories and drive our emotions and decisions. Being part of Sapien Labs is, in this sense, a personal journey that has brought, and continues to bring, new perspectives to this understanding. What also captures my imagination though is the possibility of turning these insights and ideas into something real that can integrate tangibly into the world at scale to help millions of people better navigate their mental challenges. What are the mechanisms to get us there faster? What are models that can work? Solving these challenges in the context of a smart, committed team is what I find most interesting and rewarding.

Dr. Shailender Swaminathan

Director, Sapien Labs Centre for Human Brain and Mind at Krea University

Dr. Shailender Swaminathan is the Director of the Sapien Labs Centre for Human Brain and Mind at Krea University. His expertise lies in Health Economics and Policy and applied econometrics.

Shailender returned to India after two decades in the United States where he held diverse academic and research positions at the University of Michigan Ann Arbor, the University of Alabama, Birmingham, and Brown University’s Department of Health Policy. His work has spanned various domains of health policy including understanding the impact of health insurance expansions on utilization and health in both the United States and India. Most recently he was Dean of Arts and Sciences at Sai University.

His present interests lie in bringing together cross-disciplinary perspectives to develop insights into brain and mind health that can be translated into impactful public policy and programs at large scale.

Shailender has a PhD in Economics from University of Southern California and is an adjunct faculty member in Brown University.

Mental health is crucial to the successful functioning of society. Yet there is only a limited understanding of how social, economic, and environmental factors determine mental health. The absence of high quality large-scale data is a huge constraint, especially in the context of low-and middle-income countries. What draws me to Sapien Labs is the focus on large-scale data that combines EEG recordings of the brain, self-reported mental health, and detailed information on the social, economic and environmental conditions under which individuals live with cross-disciplinary perspectives and rigorous analysis to help address questions of fundamental importance in the current, global context. I see this as a unique opportunity to understand the drivers of mental health and am eager to see how this work can be used to inform government policy.

Board Member

As a Board Member at Sapien Labs Randall Winn brings a perspective on building data and analytics organizations across a wide range of end markets, including healthcare. Randall is the co-founder and Managing Partner at 22C, a private investment firm delivering equity capital, strategic guidance, tactical mentorship and targeted operational resources to data/information and business services companies. 22C’s operational and technology resources, including a 100+ person affiliated data science organization, deliver practical, real-world support to 22C’s management team and partners in their efforts to build companies that are leaders in their respective markets.

Randy was co-founder and long-time CEO of Capital IQ. He is a current board member of ZoomInfo, Aurora Energy Research, LMI, and Canoe Software. He was the former Chairman of Dealogic and was a former board member of Definitive Healthcare, Viteos Fund Services and Merit Software. Randy received his bachelor’s degree from Princeton University.

As a father of three kids, I have been deeply concerned by the degradation in mental health over the past years, particularly among teens. I had developed a series of hypotheses about the causes and the extent of the problems, but was lacking data to really understand it. Given my background and “day job” of building data and analytics businesses, the lack of data was deeply unsatisfying. After meeting Tara and team and seeing the work they have done, I wanted to join the effort to shine a light on the causes of this issue and bring my experience to try to make an impact.

Rahul Varma

Rahul is a global Human Resources leader, speaker and thought leader whose career has been dedicated to helping people thrive at work and make a positive impact on businesses and society. He has led large, globally distributed workforces and complex organizational transformations. Most recently, Rahul was the Chief Talent Officer for Accenture Technology, leading HR for Accenture’s largest business that employs over 375,000 people. From India to Singapore to New York he has previously held various roles at Accenture including Global Head of Talent, Chief Learning Officer, Global Head of HR Strategy and Head of HR, India. During this journey, he crafted a new approach to leadership development, implemented an AI based solution to measuring skills, helped launch and integrate Accenture’s platform business, led the largest scale successful re-imagination of performance management in the world and created multiple innovations in digital learning and global learning centers. As the first HR Director in India, Rahul led through a phase of dramatic growth as Accenture grew from 200 people to almost 40,000 in seven years, now a Harvard Business School case study on hyper-growth in India. Rahul has also been a two-time recipient of the First-Place CLO Learning Elite Award.

Rahul has served on the boards of Covenant House International, a non-profit dedicated to eradicating youth homelessness and Madura Microfinance, which provided microloans across rural India.

Rahul holds a master’s degree in Human Resources from Symbiosis Institute of Business Management (Pune, India) and a bachelor’s degree in Economics from University of Delhi.

I have come to realize that we need to evolve our current systems and structures, within organizations and more broadly in society, for people to lead lives that are physically healthy, intellectually energizing, emotionally fulfilling, and fundamentally aligned to their individual purpose. I seek to create solutions for better ways to work and live by fusing the latest understanding from modern science with enduring wisdom. Sapien Labs is doing profoundly important work in bringing the richest and most comprehensive data to inform the state of human wellbeing around the world and its drivers that I believe can provide direction in fulfilling this goal.

Uttara Bharath

Senior Technical Advisor for Social and Behavior Change, Johns Hopkins Center for Communication Programs

Uttara brings a global public health perspective to Sapien Labs, particularly with respect to practical ways to deliver research insights in cognitive and mental health as effective public health communication.

For the last two decades, as part of the Johns Hopkins Center for Communication Programs Uttara has lived and worked across Africa and Asia focused on the design and implementation of various public health communication programs addressing the behavioral, social and normative aspects of a range of health issues including HIV/AIDS and other infectious diseases, Maternal and Child Health, Suicide Prevention, Child Abuse, Early Childhood Development and more recently COVID-19. She is also the founder of Nalamdana, a non-profit for health communication in India, which she founded in 1993 as an echoing green fellow. She continues to serve on Nalamdana’s Board of Trustees providing technical guidance to its projects, research, and planning, as well as training and workshops.

Uttara holds an MPH from the Johns Hopkins Bloomberg School of Public Health, and a BA in English from Wellesley College.

“Given my area of expertise in behavioural science, I am particularly interested in the social determinants of cognitive development – the enablers, inhibitors and barriers. Understanding these better will go a long way in strengthening caregiving and education strategies in early childhood, childhood and adolescence.”

Callyn Giese

Senior Manager, Marketing and Events

Callyn joined Sapien Labs in 2019 and manages marketing and events, including coordination of all virtual symposia which she has helped design and launch. She also supports the marketing outreach of the WorkforceMHQ product.

She has over 10 years of experience working in marketing and events and previously managed large trade shows and hosted events, including all aspects of event strategy, planning, execution, marketing and communication. One of her proudest achievements was the creation of a “Women in Analytics” panel at the annual client conference in her previous role at Applied Predictive Technologies, a Mastercard Company.

Callyn has an M.A. in English from George Mason University and a B.A. in English from James Madison University.

It is an honor to be part of an organization that has amazing ambitions – improving mental health on a global scale. It’s rewarding to be able to use marketing and events skills honed in the corporate world to support that mission. I’m also grateful to be part of a team that blends an academic background in neuroscience with an entrepreneurial spirit in a really cool and unique way.

Joseph Taylor

Head of Digital Marketing

Joe leads global digital outreach for Sapien Labs particularly enabling broad multi-language global outreach for the Mental Health Million Project and the MHQ products for Workforce and Universities.

Joe has broad experience in International marketing and media where he has previously led high value projects & large cross-disciplinary teams in digital marketing across multiple industries and Fortune 500 companies including Entertainment (20th Century Fox, Sony, YouTube), Technology (Google, Microsoft) and FMCG (Nestle, P&G, RB). He is certified across multiple digital marketing platforms.

He has also volunteered as a Teacher/Tutor with Breakthrough SF – a community program designed to support children from diverse and less-privileged backgrounds to have a path to a college education.

Joe has a B.A. in Law and M.Sc. in Money and Banking from the University of Birmingham, UK and a Diploma in French Law from Universite de Limoges, France.

It is highly rewarding to be working with an organization that is driving real change in our understanding of mental health and wellbeing. The Sapien Labs mission is fundamental to our collective future, and as a marketer, being able to contribute even a little is an incredible opportunity. Having worked in marketing and communications for nearly 20 years, I’ve contributed to the growth of multiple companies, brands and products. My goal at Sapien Labs is to apply the techniques of reaching the right people with the right message, to an audience that is as large and diverse as the entire planet.

Narayan Subramaniyam, Ph.D.

Research Affiliate

Narayan’s focus at Sapien Labs is on developing novel analytical approaches to the EEG signal. He is also the primary author of the popular EEG methods articles on Lab Talk.

Narayan has a longstanding interest in complex systems, statistical signal processing, computational modeling and nonlinear time series analysis, with applications to functional neuroimaging data (EEG, MEG), including brain connectivity estimation. He has previously worked on quantifying structural properties of EEG data based on complex networks and nonlinear dynamics, with applications to derive EEG-based biomarkers for epilepsy.

Prior to his scientific study he worked as a software engineer at MindTree, India on end-to-end SAP implementations for businesses. His other interests include pedagogical tools for teachers in higher education.

Narayan has an M.Sc. and Ph.D. in Biomedical Engineering from the Tampere University of Technology, Finland and a B.E. in Electrical and Electronic Engineering from the BMS College of Engineering in Bangalore, India. He is also presently a researcher at Tampere University of Technology, Finland.

“I am passionate about solving problems at the intersection of mathematics and neuroscience. At Sapien labs, I get the amazing opportunity to challenge and test the fundamental assumptions made when applying mathematical methods to analyze and interpret EEG data.”

Olesia Topalo

Manager, Data Operations and Workflow

Olesia manages all of Sapien Labs’ backend data and technology processes, workflows and QA.

She has extensive experience in the design of workflows and in QA for software products. She is a Certified Test Manager with deep knowledge of manual and automation testing of software and has served as a project manager and QA Lead on various large software development projects with high pressure delivery schedules. She has knowledge of a wide range of software, testing and tracking tools and programming languages.

Olesia also speaks a number of languages including English, German, Russian, Ukranian and Polish and is a certified German to Ukranian translator.

She has both a Bachelor and Master’s Degree in Translation (German-Ukrainian) from the Foreign Languages Department at Lviv National University of Ivan Franko in Ukraine.

I am excited about working with Sapien Labs because I believe in the goals and mission of the organization. I feel that the research and work done by Sapien Labs can impact the world and help people understand the brain and improve our mental wellbeing. I am working with data operation and management activities and it’s really fascinating how much dependency we can find between our mental wellbeing and environmental factors.

Jerzy Bala, Ph.D.

Chief Data Scientist

Jerzy has a personal interest in brain health issues and joins Sapien Labs to apply his computational expertise to enable better brain health.

He brings deep perspective to solving complex data analytics problems. His wide breadth of expertise includes artificial intelligence, machine learning, natural language processing, healthcare analytics and probabilistic modeling that he has applied across diverse domains such financial fraud prevention and detection and risk management, next product to buy predictions and sensor analytics for ballistic target discrimination. He has served as Principal Investigator for research projects under the aegis of various government agencies including Defense Research Projects Agency, National Geospatial-Intelligence Agency, Department of Education, and the U.S. Department of Veterans Affairs. Jerzy has received ten Commonwealth of Virginia Outstanding Achievement Awards for success in the U.S. Department of Defense research projects.

He has conceived several patented machine learning algorithms, among them, distributed data and text mining methods, and a data analytics method geared towards the interactive acquisition and display of visual knowledge representations. He is also the co-author of the book, Machine Learning – A Multi strategy Approach. His postdoctoral research was sponsored through a grant from the National Science Foundation in Computational Science and Engineering to investigate a class of multi-strategy machine learning algorithms that combine explainable rule-based learning with neural networks.

He has a Ph.D. in Computer Science from George Mason University, and a B.S. and M.S. in Electrical Engineering from the AGH University of Science and Technology in Krakow, Poland. He has held various roles in data science and analytics, most recently as Vice President, Analytics at Bottomline Technologies.

Sapien Labs is in a unique position of executing ongoing global data acquisition initiatives to builda rich and evolving collection of large-scale multidimensional data sets that include demographics with life experience, neurophysiological, and mental health information. As a data scientist, I understand the importance of such data as an asset that can fuel better understanding of the brain, leading to better tools for more accurate diagnoses and treatment of disorders. I am most interested in solving complex knowledge and large-scale data analytics problems towards efforts to build proof-of-concept demonstrations and productization of analytical solutions.

Dhanya Parameshwaran, Ph.D.

Senior Scientist

Dhanya’s combined interest in solving socioeconomic challenges and understanding the brain led to the pioneering work behind the Human Brain Diversity Project where she continues to build deep insights into the EEG signal and its relationship to environment and human outcomes.

Dhanya has worked across various neural systems and species from rats to humans studying electrical activity of the brain from spiking activity in CA1 pyramidal neuronal networks to LFP, ECoGand EEG signals. She has a broad computational background with substantial experience in signal processing and machine learning, as well as extensive programming experience in MATLAB, R, and Python. Previously, as a Data Scientist for Madura Microfinance in India, she developed analytical models to predict the economic success of informal rural economies.

Dhanya is also co-Founder of Kabbadi Adda, an active organizer of the Kabbadi leagues in India.

Dhanya has a B.Tech from the Indian Institute of Technology (IIT), Madras in Aerospace Engineering and a Ph.D. in Neuroscience from the National Center for Biological Sciences (NCBS), TIFR in India.

The more we find out about the brain, the more uncharted the science seems. Often in neuroscience, we generalize our findings to humanity as a whole based on information from a few select individuals chosen for research. I am interested in the differences across individuals as opposed to the similarities. At Sapien Labs, I get an opportunity to look at large-scale data from India, US, Sudan, Argentina and beyond. Being able to connect the evolution of the brain to the evolution of man is a question I am fascinated by. At Sapien Labs I am involved in building tools to analyze EEG data quickly and arrive at meaningful research outcomes. Simplifying EEG analysis and being able to build EEG tools that are deployed for everyone to use on a large scale, agnostic to the question, is something I am keen to contribute”.

Tara Thiagarajan, Ph.D.

Founder and Chief Scientist

Tara founded Sapien Labs as a way to bring together diverse disciplines and domains to build deep, holistic and global understanding of our evolving brain and mind that can impact our individual and societal health and wellbeing in practical and positive ways.

Over the last decades she has looked for insights into the nature of brain and mind across species and from multiple perspectives. From this multifaceted view she takes a complex systems perspective and is guided by two overarching insights: that the integrated system is far more than the sum of its parts, and that our changing environment is driving an evolving divergence of brain physiology among us with health and societal consequences that are more profound than we have appreciated.

Until March 2021, Tara also led Madura Microfinance building it from its founding into an organization with 3,000 people reaching into over 25,000 villages and small towns across India to provide small loans to over a million people each year. At Madura she pioneered data and analytical frameworks to enable insights into economic outcomes in these data dark ecosystems, and lived the unique challenges of building an organization that integrates across the full breadth of humanity from the very poorest, least educated and off-grid to the wealthy, educated and technology savvy. Altogether this has contributed to a global approach to science that is grounded in real-world challenges and implementation.

Tara has a Ph.D. in Neuroscience from Stanford University, a B.A. in Mathematics from Brandeis University and an MBA from the Kellogg School of Management at Northwestern University. Previously she was also a post-doctoral fellow at the National Institutes of Health (NIH) in the Section on Critical Brain Dynamics, a Visiting Scientist at the National Center for Biological Sciences in India and has worked in Strategic Scientific Planning at Bristol Myers-Squibb.

“I’ve explored the nature of brain and mind from so many perspectives over the last decades – from the molecular and cellular physiology of neurons, electrode arrays implanted in monkey brains and EEG signals in humans, to the behavior of human systems from small informal rural economies to the building and managing of teams and organizations. And of course, observing the nature and meanderings of my own mind and relationship to the world. Across all of this the quest has always been for an overarching framework to understand the place of the brain and mind in the creation of the world in a way that integrates across all of its diverse outcomes. As I see it, Sapien Labs is an adventure in understanding, but also an important practical effort to help us guide our individual and collective human journey.”

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Attention in psychology, neuroscience, and machine learning.

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Corrigendum: Attention in Psychology, Neuroscience, and Machine Learning

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• Gatsby Computational Neuroscience Unit, Sainsbury Wellcome Centre, University College London, London, United Kingdom

Attention is the important ability to flexibly control limited computational resources. It has been studied in conjunction with many other topics in neuroscience and psychology including awareness, vigilance, saliency, executive control, and learning. It has also recently been applied in several domains in machine learning. The relationship between the study of biological attention and its use as a tool to enhance artificial neural networks is not always clear. This review starts by providing an overview of how attention is conceptualized in the neuroscience and psychology literature. It then covers several use cases of attention in machine learning, indicating their biological counterparts where they exist. Finally, the ways in which artificial attention can be further inspired by biology for the production of complex and integrative systems is explored.

1. Introduction

Attention is a topic widely discussed publicly and widely studied scientifically. It has many definitions within and across multiple fields including psychology, neuroscience, and, most recently, machine learning ( Chun et al., 2011 ; Cho et al., 2015 ). As William James wrote at the dawn of experimental psychology, “Everyone knows what attention is. It is the taking possession by the mind, in clear, and vivid form, of one out of what seems several simultaneously possible objects or trains of thought.” Since James wrote this, many attempts have been made to more precisely define and quantify this process while also identifying the underlying mental and neural architectures that give rise to it. The glut of different experimental approaches and conceptualizations to study what is spoken of as a single concept, however, has led to something of a backlash amongst researchers. As was claimed in the title of a recent article arguing for a more evolution-informed approach to the concept, “No one knows what attention is” ( Hommel et al., 2019 ).

Attention is certainly far from a clear or unified concept. Yet despite its many, vague, and sometimes conflicting definitions, there is a core quality of attention that is demonstrably of high importance to information processing in the brain and, increasingly, artificial systems. Attention is the flexible control of limited computational resources. Why those resources are limited and how they can best be controlled will vary across use cases, but the ability to dynamically alter and route the flow of information has clear benefits for the adaptiveness of any system.

The realization that attention plays many roles in the brain makes its addition to artificial neural networks unsurprising. Artificial neural networks are parallel processing systems comprised of individual units designed to mimic the basic input-output function of neurons. These models are currently dominating the machine learning and artificial intelligence (AI) literature. Initially constructed without attention, various mechanisms for dynamically re-configuring the representations or structures of these networks have now been added.

The following section, section 2, will cover broadly the different uses of the word attention in neuroscience and psychology, along with its connection to other common neuroscientific topics. Throughout, the conceptualization of attention as a way to control limited resources will be highlighted. Behavioral studies will be used to demonstrate the abilities and limits of attention while neural mechanisms point to the physical means through which these behavioral effects are manifested. In section 3, the state of attention research in machine learning will be summarized and relationships between artificial and biological attention will be indicated where they exist. And in section 4 additional ways in which findings from biological attention can influence its artificial counterpart will be presented.

The primary aim of this review is to give researchers in the field of AI or machine learning an understanding of how attention is conceptualized and studied in neuroscience and psychology in order to facilitate further inspiration where fruitful. A secondary aim is to inform those who study biological attention how these processes are being operationalized in artificial systems as it may influence thinking about the functional implications of biological findings.

2. Attention in Neuroscience and Psychology

The scientific study of attention began in psychology, where careful behavioral experimentation can give rise to precise demonstrations of the tendencies and abilities of attention in different circumstances. Cognitive science and cognitive psychology aim to turn these observations into models of how mental processes could create such behavioral patterns. Many word models and computational models have been created that posit different underlying mechanisms ( Driver, 2001 ; Borji and Itti, 2012 ).

The influence of single-cell neurophysiology in non-human primates along with non-invasive means of monitoring human brain activity such as EEG, fMRI, and MEG have made direct observation of the underlying neural processes possible. From this, computational models of neural circuits have been built that can replicate certain features of the neural responses that relate to attention ( Shipp, 2004 ).

In the following sub-sections, the behavioral and neural findings of several different broad classes of attention will be discussed.

2.1. Attention as Arousal, Alertness, or Vigilance

In its most generic form, attention could be described as merely an overall level of alertness or ability to engage with surroundings. In this way it interacts with arousal and the sleep-wake spectrum. Vigilance in psychology refers to the ability to sustain attention and is therefore related as well. Note, while the use of these words clusters around the same meaning, they are sometimes used more specifically in different niche literature ( Oken et al., 2006 ).

Studying subjects in different phases of the sleep-wake cycle, under sleep deprivation, or while on sedatives offers a view of how this form of attention can vary and what the behavioral consequences are. By giving subjects repetitive tasks that require a level of sustained attention—such as keeping a ball within a certain region on a screen—researchers have observed extended periods of poor performance in drowsy patients that correlate with changes in EEG signals ( Makeig et al., 2000 ). Yet, there are ways in which tasks can be made more engaging that can lead to higher performance even in drowsy or sedated states. This includes increasing the promise of reward for performing the task, adding novelty or irregularity, or introducing stress ( Oken et al., 2006 ). Therefore, general attention appears to have limited reserves that won't be deployed in the case of a mundane or insufficiently rewarding task but can be called upon for more promising or interesting work.

Interestingly, more arousal is not always beneficial. The Yerkes-Dodson curve ( Figure 1B ) is an inverted-U that represents performance as a function of alertness on sufficiently challenging tasks: at low levels of alertness performance is poor, at medium levels it is good, and at high levels it becomes poor again. The original study used electric shocks in mice to vary the level of alertness, but the finding has been repeated with other measures ( Diamond, 2005 ). It may explain why psychostimulants such as Adderall or caffeine can work to increase focus in some people at some doses but become detrimental for others ( Wood et al., 2014 ).

Figure 1 . General attention and alertness (A) Cells in the locus coeruleus release norepinephrine (also known as noradrenaline) onto many parts of the brain with different functions, including onto other neuromodulatory systems. This contributes to overall arousal ( Samuels and Szabadi, 2008 ). Colors here represent different divisions of the brain: forebrain (green), diencephalon (yellow), and brainstem (blue). (B) The Yerkes-Dodson curve describes the nonlinear relationship between arousal and performance on challenging tasks.

The neural circuits underlying the sleep-wake cycle are primarily in the brain stem ( Coenen, 1998 ). These circuits control the flow of information into the thalamus and then onto cortex. Additionally, neuromodulatory systems play a large role in the control of generalized attention. Norepinephrine, acetylcholine, and dopamine are believed to influence alertnesss, orienting to important information, and executive control of attention, respectively ( Posner, 2008 ). The anatomy of neuromodulators matches their function as well. Neurons that release norepinephrine, for example, have their cell bodies in the brain stem but project very broadly across the brain, allowing them to control information processing broadly ( Figure 1A ).

2.2. Sensory Attention

In addition to overall levels of arousal and alertness, attention can also be selectively deployed by an awake subject to specific sensory inputs. Studying attention within the context of a specific sensory system allows for tight control over both stimuli and the locus of attention. Generally, to look for this type of attention the task used needs to be quite challenging. For example, in a change detection task, the to-be-detected difference between two stimuli may be very slight. More generally, task difficulty can be achieved by presenting the stimulus for only a very short period of time or only very weakly.

A large portion of the study of attention in systems neuroscience and psychology centers on visual attention in particular ( Kanwisher and Wojciulik, 2000 ). This may reflect the general trend in these fields to emphasis the study of visual processing over other sensory systems ( Hutmacher, 2019 ), along with the dominant role vision plays in the primate brain. Furthermore, visual stimuli are frequently used in studies meant to address more general, cognitive aspects of attention as well.

Visual attention can be broken down broadly into spatial and feature-based attention.

2.2.1. Visual Spatial Attention

Saccades are small and rapid eye movements made several times each second. As the fovea offers the highest visual resolution on the retina, choosing where to place it is essentially a choice about where to deploy limited computational resources. In this way, eye movements indicate the locus of attention. As this shift of attention is outwardly visible it is known as overt visual attention.

By tracking eye movements as subjects are presented with different images, researchers have identified image patterns that automatically attract attention. Such patterns are defined by oriented edges, spatial frequency, color contrast, intensity, or motion ( Itti and Koch, 2001 ). Image regions that attract attention are considered “salient” and are computed in a “bottom-up” fashion. That is, they don't require conscious or effortful processing to identify and are likely the result of built-in feature detectors in the visual system. As such, saliency can be computed very quickly. Furthermore, different subjects tend to agree on which regions are salient, especially those identified in the first few saccades ( Tatler et al., 2005 ).

Salient regions can be studied in “free-viewing” situations, that is, when the subject is not given any specific instructions about how to view the image. When a particular task is assigned, the interplay between bottom-up and “top-down” attention becomes clear. For example, when instructed to saccade to a specific visual target out of an array, subjects may incorrectly saccade to a particularly salient distractor instead ( van Zoest and Donk, 2005 ). More generally, task instructions can have a significant effect on the pattern of saccades generated when subjects are viewing a complex natural image and given high-level tasks (e.g., asked to assess the age of a person or guess their socio-economic status). Furthermore, the natural pattern of eye movements when subjects perform real world tasks, like sandwich making, can provide insights to underlying cognitive processes ( Hayhoe and Ballard, 2005 ).

When subjects need to make multiple saccades in a row they tend not to return to locations they have recently attended and may be slow to respond if something relevant occurs there. This phenomenon is known as inhibition of return ( Itti and Koch, 2001 ). Such behavior pushes the visual system to not just exploit image regions originally deemed most salient but to explore other areas as well. It also means the saccade generating system needs to have a form of memory; this is believed to be implemented by short-term inhibition of the representation of recently-attended locations.

While eye movements are an effective means of controlling visual attention, they are not the only option. “Covert” spatial attention is a way of emphasizing processing of different spatial locations without an overt shift in fovea location. Generally, in the study of covert spatial attention, subjects must fixate on a central point throughout the task. They are cued to covertly attend to a location in their peripheral vision where stimuli relevant for their visual task will likely appear. For example, in an orientation discrimination task, after the spatial cue is provided an oriented grating will flash in the cued location and the subject will need to indicate its orientation. On invalidly-cued trials (when the stimulus appears in an uncued location), subjects perform worse than on validly-cued (or uncued) trials ( Anton-Erxleben and Carrasco, 2013 ). This indicates that covert spatial attention is a limited resource that can be flexibly deployed and aids in the processing of visual information.

Covert spatial attention is selective in the sense that certain regions are selected for further processing at the expense of others. This has been referred to as the “spotlight” of attention. Importantly, for covert—as opposed to overt—attention the input to the visual system can be identical while the processing of that input is flexibly selective.

Covert spatial attention can be impacted by bottom-up saliency as well. If an irrelevant but salient object is flashed at a location that then goes on to have a task relevant stimulus, the exogenous spatial attention drawn by the irrelevant stimulus can get applied to the task relevant stimulus, possibly providing a performance benefit. If it is flashed at an irrelevant location, however, it will not help, and can harm performance ( Berger et al., 2005 ). Bottom-up/exogenous attention has a quick time course, impacting covert attention for 80–130 ms after the distractor appears ( Anton-Erxleben and Carrasco, 2013 ).

In some theories of attention, covert spatial attention exists to help guide overt attention. Particularly, the pre-motor theory of attention posits that the same neural circuits plan saccades and control covert spatial attention ( Rizzolatti et al., 1987 ). The frontal eye field (FEF) is known to be involved in the control of eye movements. Stimulating the neurons in FEF at levels too low to evoke eye movements has been shown to create effects similar to covert attention ( Moore et al., 2003 ). In this way, covert attention may be a means of deciding where to overtly look. The ability to covertly attend may additionally be helpful in social species, as eye movements convey information about knowledge and intent that may best be kept secret ( Klein et al., 2009 ).

To study the neural correlates of covert spatial attention, researchers identify which aspects of neural activity differ based only on differences in the attentional cue (and not on differences in bottom-up features of the stimuli). On trials where attention is cued toward the receptive field of a recorded neuron, many changes in the neural activity have been observed ( Noudoost et al., 2010 ; Maunsell, 2015 ). A commonly reported finding is an increase in firing rates, typically of 20–30% ( Mitchell et al., 2007 ). However, the exact magnitude of the change depends on the cortical area studied, with later areas showing stronger changes ( Luck et al., 1997 ; Noudoost et al., 2010 ). Attention is also known to impact the variability of neural firing. In particular, it decreases trial-to-trial variability as measured via the Fano Factor and decreases noise correlations between pairs of neurons. Attention has even been found to impact the electrophysiological properties of neurons in a way that reduces their likelihood of firing in bursts and also decreases the height of individual action potentials ( Anderson et al., 2013 ).

In general, the changes associated with attention are believed to increase the signal-to-noise ratio of the neurons that represent the attended stimulus, however they can also impact communication between brain areas. To this end, attention's effect on neural synchrony is important. Within a visual area, attention has been shown to increase spiking coherence in the gamma band—that is at frequencies between 30 and 70 Hz ( Fries et al., 2008 ). When a group of neurons fires synchronously, their ability to influence shared downstream areas is enhanced. Furthermore, attention may also be working to directly coordinate communication across areas. Synchronous activity between two visual areas can be a sign of increased communication and attention has been shown to increase synchrony between the neurons that represent the attended stimulus in areas V1 and V4, for example ( Bosman et al., 2012 ). Control of this cross-area synchronization appears to be carried out by the pulvinar ( Saalmann et al., 2012 ).

In addition to investigating how attention impacts neurons in the visual pathways, studies have also searched for the source of top-down attention ( Noudoost et al., 2010 ; Miller and Buschman, 2014 ). The processing of bottom-up attention appears to culminate with a saliency map produced in the lateral intraparietal area (LIP). The cells here respond when salient stimuli are in their receptive field, including task-irrelevant but salient distractors. Prefrontal areas such as FEF, on the other hand, appear to house the signals needed for top-down control of spatial attention and are less responsive to distractors.

While much of the work on the neural correlates of sensory attention focuses on the cortex, subcortical areas appear to play a strong role in the control and performance benefits of attention as well. In particular, the superior colliculus assists in both covert and overt spatial attention and inactivation of this region can impair attention ( Krauzlis et al., 2013 ). And, as mentioned above, the pulvinar plays a role in attention, particularly with respect to gating effects on cortex ( Zhou et al., 2016 ).

2.2.2. Visual Feature Attention

Feature attention is another form of covert selective attention. In the study of feature attention, instead of being cued to attend to a particular location, subjects are cued on each trial to attend to a particular visual feature such as a specific color, a particular shape, or a certain orientation. The goal of the task may be to detect if the cued feature is present on the screen or readout another one of its qualities (e.g., to answer “what color is the square?” should result in attention first deployed to squares). Valid cueing about the attended feature enhances performance. For example, when attention was directed toward a particular orientation, subjects were better able to detect faint gratings of that orientation than of any other orientation ( Rossi and Paradiso, 1995 ). While the overall task (e.g., detection of an oriented grating) remains the same, the specific instructions (detection of 90° grating vs. 60° vs. 30°) will be cued on each individual trial, or possibly blockwise. Successful trial-wise cueing indicates that this form of attention can be flexibly deployed on fast timescales.

Visual search tasks are also believed to activate feature-based attention ( Figure 2 ). In these tasks, an array of stimuli appears on a screen and subjects need to indicate—frequently with an eye movement—the location of the cued stimulus. As subjects are usually allowed to make saccades throughout the task as they search for the cued stimulus, this task combines covert feature-based attention with overt attention. In fact, signals of top-down feature-based attention have been found in FEF, the area involved in saccade choice ( Zhou and Desimone, 2011 ). Because certain features can create a pop-out effect—for example, a single red shape amongst several black ones will immediately draw attention—visual search tasks also engage bottom-up attention which, depending on the task, may need to be suppressed ( Wolfe and Horowitz, 2004 ).

Figure 2 . Visual search tasks engage many forms of visual attention. Across the top row the progression of a visual search task is shown. First, a cue indicates the target of the visual search, in this case a blue X. Then a search array appears with many non-targets. Top-down feature attention to cells that represent the color blue and the shape X will increase their firing throughout the visual field but firing will be strongest where blue or Xs actually occur. These neural response will play a role in generating a map of covert spatial attention which can be used to explore visual space before saccading. After the shift in overt attention with the first saccade, the covert attention map is remade. Finally, the target is located and successfully saccaded to. If the visual array contained a pop-out stimulus (for example a green O) it may have captured covert spatial attention in a bottom-up way and led to an additional incorrect saccade.

Neural effects of feature-based attention in the visual system are generally similar to those of spatial attention. Neurons that represent the attended feature, for example, have increased firing rates, and those that represent very different features have suppressed rates ( Treue and Trujillo, 1999 ). As opposed to spatial attention, however, feature-based attention is spatially-global. This means that when deploying attention to a particular feature the activity of the neurons that represent that feature anywhere in visual space are modulated ( Saenz et al., 2002 ). Another difference between spatial and feature attention is the question of how sources of top-down attention target the correct neurons in the visual system. The retinotopic map, wherein nearby cells represent nearby spatial locations, makes spatial targeting straightforward, but cells are not as neatly organized according to preferred visual features.

The effects of spatial and feature attention appear to be additive ( Hayden and Gallant, 2009 ). Furthermore, both feature and spatial attention are believed to create their effects by acting on the local neural circuits that implement divisive normalization in visual cortex ( Reynolds and Heeger, 2009 ). Modeling work has shown that many of the neural effects of selective attention can be captured by assuming that top-down connections provide targeted synaptic inputs to cells in these circuits ( Lindsay et al., 2019 ). However, models that rely on effects of the neuromodulator acetylcholine can also replicate neural correlates of attention ( Sajedin et al., 2019 ).

Potential sources of top-down feature-based attention have been found in prefrontal cortex where sustained activity encodes the attended feature ( Bichot et al., 2015 ; Paneri and Gregoriou, 2017 ). Inactivating the ventral prearcuate area impairs performance on search tasks. From prefrontal areas, attention signals are believed to travel in a reverse hierarchical way wherein higher visual areas send inputs to those below them ( Ahissar and Hochstein, 2000 ).

A closely related topic to feature attention is object attention. Here, attention is not deployed to an abstract feature in advance of a visual stimulus, but rather it is applied to a particular object in the visual scene ( Chen, 2012 ). The initial feedforward pass of activity through the visual hierarchy is able to pre-attentively segregate objects from their backgrounds in parallel across the visual field, provided these objects have stark and salient differences from the background. In more crowded or complex visual scenes, recurrent and serial processing is needed in order to identify different objects ( Lamme and Roelfsema, 2000 ). Serial processing involves moving limited attentional resources from one location in the image to another; it can take the form of shifts in either covert or overt spatial attention ( Buschman and Miller, 2009 ). Recurrent connections in the visual system—that is, both horizontal connections from nearby neurons in the same visual area and feedback connections from those in higher visual areas—aid in figure-ground segregation and object identification. The question of how the brain performs perceptual grouping of low-level features into a coherent object identity has been studied for nearly a century. It is believed that attention may be required for grouping, particularly for novel or complex objects ( Roelfsema and Houtkamp, 2011 ). This may be especially important in visual search tasks that require locating an object that is defined by a conjunction of several features.

Neurally, the effects of object-based attention can spread slowly through space as parts of an object are mentally traced ( Roelfsema et al., 1998 ). Switching attention to a location outside an object appears to incur a greater cost than switching to the same distance away but within the object ( Brown and Denney, 2007 ). In addition, once attention is applied to a visual object, it is believed to activate feature-based attention for the different features of that object across the visual field ( O'Craven et al., 1999 ).

Another form of attention sometimes referred to as feature attention involves attending to an entire feature dimension. An example of this is the Stroop test, wherein the names of colors are written in different colored ink and subjects either need to read the word itself or say the color of the ink. Here attention cannot be deployed to a specific feature in advance, only to the dimensions word or color. Neurally, the switch between dimensions appears to impact sensory coding in the visual stream and is controlled by frontal areas ( Liu et al., 2003 ).

2.2.3. Computational Models of Visual Attention

Visual attention, being one of the most heavily-studied topics in the neuroscience of attention, has inspired many computational models of how attention works. In general, these models synthesize various neurophysiological findings in order to help explain how the behavioral impacts of attention arise ( Heinke and Humphreys, 2005 ).

Several computational models meant to calculate saliency have been devised ( Itti and Koch, 2001 ). These models use low-level visual feature detectors—usually designed to match those in the visual system—to create an image-specific saliency map that can predict the saccade patterns of humans in response to the same image. Another approach to calculating saliency based on information theoretic first principles has also been explored and was able to account for certain visual search behaviors ( Bruce and Tsotsos, 2009 ).

Some of the behavioral and neural correlates of attention are similar whether the attention is bottom-up or top-down. In the Biased Competition Model of attention, stimuli compete against each other to dominate the neural response ( Desimone, 1998 ). Attention (bottom-up or top-down) can thus work by biasing this competition toward the stimulus that is the target of attention. While the Biased Competition Model is sometimes used simply as a “word model” to guide intuition, explicit computational instantiations of it have also been built. A hierarchical model of the visual pathway that included top-down biasing as well as local competition mediated through horizontal connections was able to replicate multiple neural effects of attention ( Deco and Rolls, 2004 ). A model embodying similar principles but using spiking neurons was also implemented ( Deco and Rolls, 2005 ).

Similar models have been constructed explicitly to deal with attribute naming tasks such as the Stroop test described above. The Selective Attention Model (SLAM), for example, has local competition in both the sensory encoding and motor output modules and can mimic known properties of response times in easier and more challenging Stroop-like tests ( Phaf et al., 1990 ).

Visual perception has been framed and modeled as a problem of Bayesian inference ( Lee and Mumford, 2003 ). Within this context, attention can help resolve uncertainty under settings where inference is more challenging, typically by modulating priors ( Rao, 2005 ). For example, in Chikkerur et al. (2010) spatial attention functions to reduce uncertainty about object identity and feature attention reduces spatial uncertainty. These principles can capture both behavioral and neural features of attention and can be implemented in a biologically-inspired neural model.

The feature similarity gain model of attention (FSGM) is a description of the neural effects of top-down attention that can be applied in both the feature and spatial domain ( Treue and Trujillo, 1999 ). It says that the way in which a neuron's response is modulated by attention depends on that neuron's tuning. Tuning is a description of how a neuron responds to different stimuli, so according to the FSGM a neuron that prefers (that is, responds strongly to), e.g., the color blue, will have its activity enhanced by top-down attention to blue. The FSGM also says attention to non-preferred stimuli will cause a decrease in firing and that, whether increased or decreased, activity is scaled multiplicatively by attention. Though not initially defined as a computational model, this form of neural modulation has since been shown through modeling to be effective at enhancing performance on challenging visual tasks ( Lindsay and Miller, 2018 ).

Other models conceptualize attention as a dynamic routing of information through a network. An implementation of this form of attention can be found in the Selective Attention for Identification Model (SAIM) ( Heinke and Humphreys, 2003 ). Here, attention routes information from the retina to a representation deemed the “focus of attention”; depending on the current task, different parts of the retinal representation will be mapped to the focus of attention.

2.2.4. Attention in Other Sensory Modalities

A famous example of the need for selective attention in audition is the “cocktail party problem”: the difficulty of focusing on the speech from one speaker in a crowded room of multiple speakers and other noises ( Bronkhorst, 2015 ). Solving the problem is believed to involve “early” selection wherein low level features of a voice such as pitch are used to determine which auditory information is passed on for further linguistic processing. Interestingly, selective auditory attention has the ability to control neural activity at even the earliest level of auditory processing, the cochlea ( Fritz et al., 2007 ).

Spatial and feature attention have also been explored in the somatosensory system. Subjects cued to expect a tap at different parts on their body are better able to detect the sensation when that cue is valid. However, these effects seem weaker than they are in the visual system ( Johansen-Berg and Lloyd, 2000 ). Reaction times are faster in a detection task when subjects are cued about the orientation of a stimulus on their finger ( Schweisfurth et al., 2014 ).

In a study that tested subjects' ability to detect a taste they had been cued for it was shown that validly-cued tastes can be detected at lower concentrations than invalidly-cued ones ( Marks and Wheeler, 1998 ). This mimics the behavioral effects found with feature-based visual attention. Attention to olfactory features has not been thoroughly explored, though visually-induced expectations about a scent can aid its detection ( Gottfried and Dolan, 2003 ; Keller, 2011 ).

Attention can also be spread across modalities to perform tasks that require integration of multiple sensory signals. In general, the use of multiple congruent sensory signals aids detection of objects when compared to relying only on a single modality. Interestingly, some studies suggest that humans may have a bias for the visual domain, even when the signal from another domain is equally valid ( Spence, 2009 ). Specifically, the visual domain appears to dominate most in tasks that require identifying the spatial location of a cue ( Bertelson and Aschersleben, 1998 ). This can be seen most readily in ventriloquism, where the visual cue of the dummy's mouth moving overrides auditory evidence about the true location of the vocal source. Visual evidence can also override tactile evidence, for example, in the context of the rubber arm illusion ( Botvinick and Cohen, 1998 ).

Another effect of the cross-modal nature of sensory processing is that an attentional cue in one modality can cause an orienting of attention in another modality ( Spence and Driver, 2004 ). Generally, the attention effects in the non-cued modality are weaker. This cross-modal interaction can occur in the context of both endogenous (“top-down”) and exogenous (“bottom-up”) attention.

2.3. Attention and Executive Control

With multiple simultaneous competing tasks, a central controller is needed to decide which to engage in and when. What's more, how to best execute tasks can depend on history and context. Combining sensory inputs with past knowledge in order to coordinate multiple systems for the job of efficient task selection and execution is the role of executive control, and this control is usually associated with the prefrontal cortex ( Miller and Buschman, 2014 ). As mentioned above, sources of top-down visual attention have also been located in prefrontal regions. Attention can reasonably be thought of as the output of executive control. The executive control system must thus select the targets of attention and communicate that to the systems responsible for implementing it. According to the reverse hierarchy theory described above, higher areas signal to those from which they get input which send the signal on to those below them and so on ( Ahissar and Hochstein, 2000 ). This means that, at each point, the instructions for attention must be transformed into a representation that makes sense for the targeted region. Through this process, the high level goals of the executive control region can lead to very specific changes, for example, in early sensory processing.

Executive control and working memory are also intertwined, as the ability to make use of past information as well as to keep a current goal in mind requires working memory. Furthermore, working memory is frequently identified as sustained activity in prefrontal areas. A consequence of the three-way relationship between executive control, working memory, and attention is that the contents of working memory can impact attention, even when not desirable for the task ( Soto et al., 2008 ). For example, if a subject has to keep an object in working memory while simultaneously performing a visual search for a separate object, the presence of the stored object in the search array can negatively interfere with the search ( Soto et al., 2005 ). This suggests that working memory can interfere with the executive control of attention. However, there still appears to be additional elements of that control that working memory alone does not disrupt. This can be seen in studies wherein visual search performance is even worse when subjects believe they will need to report the memorized item but are shown a search array for the attended item instead ( Olivers and Eimer, 2011 ). This suggests that, while all objects in working memory may have some influence over attention, the executive controller can choose which will have the most.

Beyond the flexible control of attention within a sensory modality, attention can also be shifted between modalities. Behavioral experiments indicate that switching attention either between two different tasks within a sensory modality (for example, going from locating a visual object to identifying it) or between sensory modalities (switching from an auditory task to a visual one) incurs a computational cost ( Pashler, 2000 ). This cost is usually measured as the extent to which performance is worse on trials just after the task has been switched vs. those where the same task is being repeated. Interestingly, task switching within a modality seems to incur a larger cost than switching between modalities ( Murray et al., 2009 ). A similar result is found when switching between or across modes of response (for example, pressing a bottom vs. verbal report), suggesting this is not specific to sensory processing ( Arrington et al., 2003 ). Such findings are believed to stem from the fact that switching within a modality requires a reconfiguration of the same neural circuits, which is more difficult than merely engaging the circuitry of a different sensory system. An efficient executive controller would need to be aware of these costs when deciding to shift attention and ideally try to minimize them; it has been shown that switch costs can be reduced with training ( Gopher, 1996 ).

The final question regarding the executive control of attention is how it evolves with learning. Eye movement studies indicate that searched-for items can be detected more rapidly in familiar settings rather than novel ones, suggesting that previously-learned associations guide overt attention ( Chun and Jiang, 1998 ). Such benefits are believed to rely on the hippocampus ( Aly and Turk-Browne, 2017 ). In general, however, learning how to direct attention is not as studied as other aspects of the attention process. Some studies have shown that subjects can enhance their ability to suppress irrelevant task information, and the generality of that suppression depends on the training procedure ( Kelley and Yantis, 2009 ). Looking at the neural correlates of attention learning, imaging results suggest that the neural changes associated with learning do not occur in the sensory pathways themselves but rather in areas more associated with attentional control ( Kelley and Yantis, 2010 ). Though not always easy to study, the development of attentional systems in infancy and childhood may provide further clues as to how attention can be learned ( Reynolds and Romano, 2016 ).

2.4. Attention and Memory

Attention and memory have many possible forms of interaction. If memory has a limited capacity, for example, it makes sense for the brain to be selective about what is allowed to enter it. In this way, the ability of attention to dynamically select a subset of total information is well-matched to the needs of the memory system. In the other direction, deciding to recall a specific memory is a choice about how to deploy limited resources. Therefore, both memory encoding and retrieval can rely on attention.

The role of attention in memory encoding appears quite strong ( Aly and Turk-Browne, 2017 ). For information to be properly encoded into memory, it is best for it be the target of attention. When subjects are asked to memorize a list of words while simultaneously engaging in a secondary task that divides their attention, their ability to consciously recall those words later is impaired (though their ability to recognize the words as familiar is not so affected) ( Gardiner and Parkin, 1990 ). Imaging studies have shown that increasing the difficulty of the secondary task weakens the pattern of activity related to memory encoding in the left ventral inferior frontal gyrus and anterior hippocampus and increases the representation of secondary task information in dorsolateral prefrontal and superior parietal regions ( Uncapher and Rugg, 2005 ). Therefore, without the limited neural processing power placed on the task of encoding, memory suffers. Attention has also been implicated in the encoding of spatially-defined memories and appears to stabilize the representations of place cells ( Muzzio et al., 2009 ).

Implicit statistical learning can also be biased by attention. For example, in Turk-Browne et al. (2005) subjects watched a stream of stimuli comprised of red and green shapes. The task was to detect when a shape of the attended color appeared twice in a row. Unbeknownst to the subjects, certain statistical regularities existed in the stream such that there were triplets of shapes likely to occur close together. When shown two sets of three shapes—one an actual co-occurring triplet and another a random selection of shapes of the same color—subjects recognized the real triplet as more familiar, but only if the triplets were from the attended color. The statistical regularities of the unattended shapes were not learned.

Yet some learning can occur even without conscious attention. For example, in Watanabe (2003) patients engaged in a letter detection task located centrally in their visual field while random dot motion was shown in the background at sub-threshold contrast. The motion had 10% coherence in a direction that was correlated with the currently-presented letter. Before and after learning this task, subjects performed an above-threshold direction classification task. After learning the task, direction classification improved only for the direction associated with the targeted letters. This suggests a reward-related signal activated by the target led to learning about a non-attended component of the stimulus.

Many behavioral studies have explored the extent to which attention is needed for memory retrieval. For example, by asking subjects to simultaneously recall a list of previously-memorized words and engage in a secondary task like card sorting, researchers can determine if memory retrieval pulls from the same limited pool of attentional resources as the task. Some such studies have found that retrieval is impaired by the co-occurrence of an attention-demanding task, suggesting it is an attention-dependent process. The exact findings, however, depend on the details of the memory and non-memory tasks used ( Lozito and Mulligan, 2006 ).

Even if memory retrieval does not pull from shared attentional resources, it is still clear that some memories are selected for more vivid retrieval at any given moment than others. Therefore, a selection process must occur. An examination of neuroimaging results suggests that the same parietal brain regions responsible for the top-down allocation and bottom-up capture of attention may play analogous roles during memory retrieval ( Wagner et al., 2005 ; Ciaramelli et al., 2008 ).

Studies of memory retrieval usually look at medium to long-term memory but a mechanism for attention to items in working memory has also been proposed ( Manohar et al., 2019 ). It relies on two different mechanisms of working memory: synaptic traces for non-attended items and sustained activity for the attended one.

Some forms of memory occur automatically and within the sensory processing stream itself. Priming is a well-known phenomenon in psychology wherein the presence of a stimulus at one point in time impacts how later stimuli are processed or interpreted. For example, the word “doctor” may be recognized more quickly following the word “hospital” than the word “school.” In this way, priming requires a form of implicit memory to allow previous stimuli to impact current ones. Several studies on conceptual or semantic priming indicate that attention to the first stimulus is required for priming effects to occur ( Ballesteros and Mayas, 2015 ); this mirrors findings that attention is required for memory encoding more generally.

Most priming is positive, meaning that the presence of a stimulus at one time makes the detection and processing of it or a related stimulus more likely at a later time. In this way, priming can be thought of as biasing bottom-up attention. However, top-down attention can also create negative priming. In negative priming, when stimuli that functioned as a distractor on the previous trial serve as the target of attention on the current trial, performance suffers ( Frings et al., 2015 ). This may stem from a holdover effect wherein the mechanisms of distractor suppression are still activated for the now-target stimulus.

Adaptation can also be considered a form of implicit memory. Here, neural responses decrease after repeated exposure to the same stimulus. By reducing the response to repetition, changes in the stimulus become more salient. Attention—by increasing the neural response to attended stimuli—counters the effects of adaptation ( Pestilli et al., 2007 ; Anton-Erxleben et al., 2013 ). Thus, both with priming and adaptation, top-down attention can overcome automatic processes that occur at lower levels which may be guiding bottom-up attention.

3. Attention in Machine Learning

While the concept of artificial attention has come up prior to the current resurgence of artificial neural networks, many of its popular uses today center on ANNs ( Mancas et al., 2016 ). The use of attention mechanisms in artificial neural networks came about—much like the apparent need for attention in the brain—as a means of making neural systems more flexible. Attention mechanisms in machine learning allow a single trained artificial neural network to perform well on multiple tasks or tasks with inputs of variable length, size, or structure. While the spirit of attention in machine learning is certainly inspired by psychology, its implementations do not always track with what is known about biological attention, as will be noted below.

In the form of attention originally developed for ANNs, attention mechanisms worked within an encoder-decoder framework and in the context of sequence models ( Cho et al., 2015 ; Chaudhari et al., 2019 ). Specifically, an input sequence will be passed through an encoder (likely a recurrent neural network) and the job of the decoder (also likely a recurrent neural network) will be to output another sequence. Connecting the encoder and decoder is an attention mechanism.

Commonly, the output of the encoder is a set of a vectors, one for each element in the input sequence. Attention helps determine which of these vectors should be used to generate the output. Because the output sequence is dynamically generated one element at a time, attention can dynamically highlight different encoded vectors at each time point. This allows the decoder to flexibly utilize the most relevant parts of the input sequence.

The specific job of the attention mechanism is to produce a set of scalar weightings, α t i , one for each of the encoded vectors ( v i ). At each step t , the attention mechanism (ϕ) will take in information about the decoder's previous hidden state ( h t −1 ) and the encoded vectors to produce unnormalized weightings:

Because attention is a limited resource, these weightings need to represent relative importance. To ensure that the α values sum to one, the unnormalized weightings are passed through a softmax:

These attention values scale the encoded vectors to create a single context vector on which the decoder can be conditioned:

This form of attention can be made entirely differentiable and so the whole network can be trained end-to-end with simple gradient descent.

This type of artificial attention is thus a form of iterative re-weighting. Specifically, it dynamically highlights different components of a pre-processed input as they are needed for output generation. This makes it flexible and context dependent, like biological attention. As such it is also inherently dynamic. While sequence modeling already has an implied temporal component, this form of attention can also be applied to static inputs and outputs (as will be discussed below in the context of image processing) and will thus introduce dynamics into the model.

In the traditional encoder-decoder framework without attention, the encoder produced a fixed-length vector that was independent of the length or features of the input and static during the course of decoding. This forced long sequences or sequences with complex structure to be represented with the same dimensionality as shorter or simpler ones and didn't allow the decoder to interrogate different parts of the input during the decoding process. But encoding the input as a set of vectors equal in length to the input sequence makes it possible for the decoder to selectively attend to the portion of the input sequence relevant at each time point of the decoding. Again, as in interpretations of attention in the brain, attention in artificial systems is helpful as a way to flexibly wield limited resources. The decoder can't reasonably be conditioned on the entirety of the input so at some point a bottleneck must be introduced. In the system without attention, the fixed-length encoding vector was a bottleneck. When an attention mechanism is added, the encoding can be larger because the bottleneck (in the form of the context vector) will be produced dynamically as the decoder determines which part of the input to attend to.

The motivation for adding such attention mechanisms to artificial systems is of course to improve their performance. But another claimed benefit of attention is interpretability. By identifying on which portions of the input attention is placed (that is, which α i values are high) during the decoding process, it may be possible to gain an understanding of why the decoder produced the output that it did. However, caution should be applied when interpreting the outputs of attention as they may not always explain the behavior of the model as expected ( Jain and Wallace, 2019 ; Wiegreffe and Pinter, 2019 ).

In the following subsections, specific applications of this general attention concept will be discussed, along with some that don't fit neatly into this framework. Further analogies to the biology will also be highlighted.

3.1. Attention for Natural Language Processing

As described above, attention mechanisms have frequently been added to models charged with processing sequences. Natural language processing (NLP) is one of the most common areas of application for sequence modeling. And, though it was not the original domain of attention in machine learning—nor does it have the most in common with biology—NLP is also one of the most common areas of application for attention ( Galassi et al., 2019 ).

An early application of the this form of attention in artificial neural networks was to the task of translation ( Bahdanau et al., 2014 ) ( Figure 3 ). In this work, a recurrent neural network encodes the input sentence as a set of “annotation” vectors, one for each word in the sentence. The output, a sentence in the target language, is generated one word at a time by a recurrent neural network. The probability of each generated word is a function of the previously generated word, the hidden state of the recurrent neural network and a context vector generated by the attention mechanism. Here, the attention mechanism is a small feedforward neural network that takes in the hidden state of the output network as well as the current annotation vector to create the weighting over all annotation vectors.

Figure 3 . Attention for neural machine translation. The to-be-translated sentence is encoded to a series of vectors ( v ) via a recurrent neural network. The attention mechanism (ϕ) uses the hidden state of the decoder ( h ) and these vectors to determine how the encoded vectors should be combined to produce a context vector ( c ), which influences the next hidden state of the decoder and thus the next word in the translated sentence.

Blending information from all the words in the sentence this way allows the network to pull from earlier or later parts when generating an output word. This can be especially useful for translating between languages with different standard word orders. By visualizing the locations in the input sentence to which attention was applied the authors observed attention helping with this problem.

Since this initial application, many variants of attention networks for language translation have been developed. In Firat et al. (2016) , the attention mechanism was adapted so it could be used to translate between multiple pairs of languages rather than just one. In Luong et al. (2015) , the authors explore different structures of attention to determine if the ability to access all input words at once is necessary. And in Cheng et al. (2016) , attention mechanisms were added to the recurrent neural networks that perform the sentence encoding and decoding in order to more flexibly create sentence representations.

In 2017, the influential “Attention is All You Need” paper utilized a very different style of architecture for machine translation ( Vaswani et al., 2017 ). This model doesn't have any recurrence, making it simpler to train. Instead, words in the sentence are encoded in parallel and these encodings generate key and query representations that are combined to create attention weightings. These weightings scale the word encodings themselves to create the next layer in the model, a process known as “self-attention.” This process repeats, and eventually interacts with the autoregressive decoder which also has attention mechanisms that allow it to flexibly focus on the encoded input (as in the standard form of attention) and on the previously generated output. The Transformer—the name given to this new attention architecture—outperformed many previous models and quickly became the standard for machine translation as well as other tasks ( Devlin et al., 2018 ).

Interestingly, self-attention has less in common with biological attention than the recurrent attention models originally used for machine translation. First, it reduces the role of recurrence and dynamics, whereas the brain necessarily relies on recurrence in sequential processing tasks, including language processing and attentional selection. Second, self-attention provides a form of horizontal interaction between words—which allows for words in the encoded sentence to be processed in the context of those around them—but this mechanism does not include an obvious top-down component driven by the needs of the decoder. In fact, self-attention has been shown under certain circumstances to simply implement a convolution, a standard feedforward computation frequently used in image processing ( Andreoli, 2019 ; Cordonnier et al., 2019 ). In this way, self-attention is more about creating a good encoding than performing a task-specific attention-like selection based on limited resources. In the context of a temporal task, its closest analogue in psychology may be priming because priming alters the encoding of subsequent stimuli based on those that came before. It is of course not the direct goal of machine learning engineers to replicate the brain, but rather to create networks that can be easily trained to perform well on tasks. These different constraints mean that even large advances in machine learning do not necessarily create more brain-like models.

While the study of attention in human language processing is not as large as other areas of neuroscience research, some work has been done to track eye movements while reading ( Myachykov and Posner, 2005 ). They find that people will look back at previous sections of text in order to clarify what they are currently reading, particularly in the context of finding the antecedent of a pronoun. Such shifts in overt attention indicate what previous information is most relevant for the current processing demands.

3.2. Attention for Visual Tasks

As in neuroscience and psychology, a large portion of studies in machine learning are done on visual tasks. One of the original attention-inspired tools of computer vision is the saliency map, which identifies which regions in an image are most salient based on a set of low-level visual features such as edges, color, or depth and how they differ from their surround ( Itti and Koch, 2001 ). In this way, saliency maps indicate which regions would be captured by “bottom-up” attention in humans and animals. Computer scientists have used saliency maps as part of their image processing pipeline to identify regions for further processing.

In more recent years, computer vision models have been dominated by deep learning. And since their success in the 2012 ImageNet Challenge ( Russakovsky et al., 2015 ), convolutional neural networks have become the default architecture for visual tasks in machine learning.

The architecture of convolutional neural networks is loosely based on the mammalian visual system ( Lindsay, 2020 ). At each layer, a bank of filters is applied to the activity of the layer below (in the first layer this is the image). This creates a H × W × C tensor of neural activity with the number of channels, C equal to the number of filters applied and H and W representing the height and width of the 2-D feature maps that result from the application of a filter.

Attention in convolutional neural networks has been used to enhance performance on a variety of tasks including classification, segmentation, and image-inspired natural language processing. Also, as in the neuroscience literature, these attentional processes can be divided into spatial and feature-based attention.

3.2.1. Spatial Attention

Building off of the structures used for attention in NLP tasks, visual attention has been applied to image captioning. In Xu et al. (2015) , the encoding model is a convolutional neural network. The attention mechanism works over the activity at the fourth convolutional layer. As each word of the caption is generated, a different pattern of weighting across spatial locations of the image representation is created. In this way, attention for caption generation replaces the set of encoded word vectors in a translation task with a set of encoded image locations. Visualizing the locations with high weights, the model appears to attend to the object most relevant to the current word being generated for the caption.

This style of attention is referred to as “soft” because it produces a weighted combination of the visual features over spatial locations ( Figure 4B ). “Hard” attention is an alternative form that chooses a single spatial location to be passed into the decoder at the expense of all others ( Figure 4A ). In Xu et al. (2015) , to decide which location should receive this hard attention, the attention weights generated for each spatial location were treated as probabilities. One location is chosen according to these probabilities. Adding this stochastic element to the network makes training more difficult, yet it was found to perform somewhat better than soft attention.

Figure 4 . Hard vs. soft visual attention in artificial neural networks. (A) In hard attention, the network only gets input from a small portion of the whole image. This portion is iteratively chosen by the network through an attention selection mechanism. If the input is foveated, the network can use the lower resolution periphery to guide this selection. (B) Feature maps in convolutional neural networks are 2-D grids of activation created by the application of a filter to the layer below. In soft spatial attention, different locations on these grids are weighted differently. In soft feature attention, different feature maps are weighted differently.

A 2014 study used reinforcement learning to train a hard attention network to perform object recognition in challenging conditions ( Mnih et al., 2014 ). The core of this model is a recurrent neural network that both keeps track of information taken in over multiple “glimpses” made by the network and outputs the location of the next glimpse. For each glimpse, the network receives a fovea-like input (central areas are represented with high resolution and peripheral with lower) from a small patch of the image. The network has to integrate the information gained from these glimpses to find and classify the object in the image. This is similar to the hard attention described above, except the selection of a location here determines which part of the image is sampled next (whereas in the case above it determined which of the already-processed image locations would be passed to the decoder). With the use of these glimpses, the network is not required to process all of the image, saving computational resources. It can also help when multiple objects are present in the image and the network must classify each ( Ba et al., 2014 ). Recent work has shown that adding a pre-training step enhances the performance of hard attention applied to complex images ( Elsayed et al., 2019 ).

In many ways, the correspondence between biological and artificial attention is strongest when it comes to visual spatial attention. For example, this form of hard attention—where different locations of the image are sequentially-sampled for further processing—replicates the process of saccading and is therefore akin to overt visual attention in the neuroscience and psychology literature. Insofar as soft attention dynamically re-weights different regions of the network's representation of the image without any change in the input to the network, it is akin to covert spatial attention. Also, as the mode of application for soft attention involves multiplicative scaling of the activity of all units at a specific location, it replicates neural findings about covert spatial attention.

Soft spatial attention has been used for other tasks, including visual question and answering ( Chen et al., 2015 ; Xu and Saenko, 2016 ; Yang et al., 2016 ) and action recognition in videos ( Sharma et al., 2015 ). Hard attention has also been used for instance segmentation ( Ren and Zemel, 2017 ) and for fine-grained classification when applied using different levels of image resolution ( Fu et al., 2017 ).

3.2.2. Feature Attention

In the case of soft spatial attention, weights are different in different spatial locations of the image representation yet they are the same across all feature channels at that location. That is, the activity of units in the network representing different visual features will all be modified the same way if they represent the same location in image space. Feature attention makes it possible to dynamically re-weight individual feature maps, creating a spatially global change in feature processing.

In Stollenga et al. (2014) , a convolutional neural network is equipped with a feature-based attention mechanism. After an image is passed through the standard feedforward architecture, the activity of the network is passed into a policy that determines how the different feature maps at different layers should be weighted. This re-weighting leads to different network activity which leads to different re-weightings. After the network has run for several timesteps the activity at the final layer is used to classify the object in the image. The policy that determines the weighting values is learned through reinforcement learning, and can be added to any pre-trained convolutional neural network.

The model in Chen et al. (2017) combines feature and spatial attention to aid in image captioning. The activity of the feedforward pass of the convolutional network is passed into the attention mechanism along with the previously generated word to create attention weightings for different channels at each layer in the CNN. These weights are used to scale activity and then a separate attention mechanism does the same procedure for generating spatial weightings. Both spatial and feature attention weights are generated and applied to the network at each time point.

In the model in De Vries et al. (2017) , the content of a question is used to control how a CNN processes an image for the task of visual question and answering. Specifically, the activity of a language embedding network is passed through a multi-layer perceptron to produce the additive and multiplicative parameters for batch normalization of each channel in the CNN. This procedure, termed conditional batch normalization, functions as a form of question-dependent feature attention.

A different form of dynamic feature re-weighting appears in “squeeze-and-excitation” networks ( Hu et al., 2018 ). In this architecture, the weightings applied to different channels are a nonlinear function of the activity of the other channels at the same layer. As with “self-attention” described above, this differs in spirit from more “top-down” approaches where weightings are a function of activity later in the network and/or biased by the needs of the output generator. Biologically speaking, this form of interaction is most similar to horizontal connections within a visual area, which are known to carry out computations such as divisive normalization ( Carandini and Heeger, 2012 ).

In the study of the biology of feature-based attention, subjects are usually cued to attend to or search for specific visual features. In this way, the to-be-attended features are known in advance and relate to the specific sub-task at hand (e.g., detection of a specific shape on a given trial of a general shape detection task). This differs from the above instances of artificial feature attention, wherein no external cue biases the network processing before knowledge about the specific image is available. Rather, the feature re-weighting is a function of the image itself and meant to enhance the performance of the network on a constant task (note this was also the case for the forms of artificial spatial attention described).

The reason for using a cueing paradigm in studies of biological attention is that it allows the experimenter to control (and thus know) where attention is placed. Yet, it is clear that even without explicit cueing, our brains make decisions about where to place attention constantly; these are likely mediated by local and long-range feedback connections to the visual system ( Wyatte et al., 2014 ). Therefore, while the task structure differs between the study of biological feature attention and its use in artificial systems, this difference may only be superficial. Essentially, the artificial systems are using feedforward image information to internally generate top-down attentional signals rather than being given the top-down information in the form of a cue.

That being said, some artificial systems do allow for externally-cued feature attention. For example setting a prior over categories in the network in Cao et al. (2015) makes it better at localizing the specific category. The network in Wang et al. (2014) , though not convolutional, has a means of biasing the detection of specific object categories as well. And in Lindsay and Miller (2018) , several performance and neural aspects of biological feature attention during a cued object detection task were replicated using a CNN. In Luo et al. (2020) , the costs and benefits of using a form of cued attention in CNNs were explored.

As mentioned above, the use of multiplicative scaling of activity is in line with certain findings from biological visual attention. Furthermore, modulating entire feature maps by the same scalar value is aligned with the finding mentioned above that feature attention acts in a spatially global way in the visual system.

3.3. Multi-Task Attention

Multi-task learning is a challenging topic in machine learning. When one network is asked to perform several different tasks—for example, a CNN that must classify objects, detect edges, and identify salient regions—training can be difficult as the weights needed to do each individual task may contradict each other. One option is have a set of task-specific parameters that modulate the activity of the shared network differently for each task. While not always called it, this can reasonably be considered a form of attention, as it flexibly alters the functioning of the network.

In Maninis et al. (2019) , a shared feedforward network is trained on all of multiple tasks, while task specific skip connections and squeeze-and-excitation blocks are trained to modulate this activity only on their specific task. This lets the network benefit from sharing processing that is common to all tasks while still specializing somewhat to each.

A similar procedure was used in Rebuffi et al. (2017) to create a network that performs classification on multiple different image domains. There, the domain could be identified from the input image making it possible to select the set of task-specific parameters automatically at run-time.

In Zhao et al. (2018) , the same image can be passed into the network and be classified along different dimensions (e.g. whether the person in the picture is smiling or not, young or old). Task-specific re-weighting of feature channels is used to execute these different classifications.

The model in Strezoski et al. (2019) uses what could be interpreted as a form of hard feature attention to route information differently in different tasks. Binary masks over feature channels are chosen randomly for each task. These masks are applied in a task-specific way during training on all tasks and at run-time. Note that in this network no task-specific attentional parameters are learned, as these masks are pre-determined and fixed during training. Instead, the network learns to use the different resulting information pathways to perform different tasks.

In a recent work, the notion of task-specific parameters was done away with entirely ( Levi and Ullman, 2020 ). Instead, the activations of a feedforward CNN are combined with a task input and passed through a second CNN to generate a full set of modulatory weights. These weights then scale the activity of the original network in a unit-specific way (thus implementing both spatial and feature attention). The result is a single set of feedforward weights capable of flexibly engaging in multiple visual tasks.

When the same input is processed differently according to many different tasks, these networks are essentially implementing a form of within-modality task switching that relies on feature attention. In this way, it is perhaps most similar to the Stroop test described previously.

3.4. Attention to Memory

Deep neural networks tend not to have explicit memory, and therefore attention to memory is not studied. Neural Turing Machines, however, are a hybrid neural architecture that includes external memory stores ( Graves et al., 2014 ). The network, through training, learns how to effectively interact with these stores to perform tasks such as sorting and repetition of stored sequences. Facilitating this interaction is a form of attention. Memories are stored as a set of vectors. To retrieve information from this store, the network generates a weight for each vector and calculates a weighted sum of the memories. To determine these weights, a recurrent neural network (which receives external and task-relevant input) outputs a vector and memories are weighted in accordance to their similarity to this vector. Thus, at each point in time, the network is able to access context-relevant memories.

As described previously, how the brain chooses what memories to attend to and then attends to them is not entirely clear. The use of a similarity metric in this model means that memories are retrieved based on their overlap with a produced activity vector, similar to associative memory models in the neuroscience literature. This offers a mechanism for the latter question—that is, how attention to memory could be implemented in the brain. The activity vector that the model produces controls what memories get attended and the relationship with biology is less clear here.

4. Ideas for Future Interaction Between Artificial and Biological Attention

As has been shown, some amount of inspiration from biology has already led to several instances of attention in artificial neural networks (summarized in Figure 5 ). While the addition of such attention mechanisms has led to appreciable increases in performance in these systems, there are clearly still many ways in which they fall short and additional opportunities for further inspiration exist. In the near term, this inspiration will likely be in the form of incremental improvements to specialized artificial systems as exist now. However, the true promise of brain-inspired AI should deliver a more integrated, multiple-purpose agent that can engage flexibly in many tasks.

Figure 5 . An incomplete summary of the different types of attention studied in neuroscience/psychology and machine learning and how they relate. On the left are divisions of attention studied biologically, on the right are those developed for artificial intelligence and machine learning. Topics at the same horizontal location are to some extent analogous, with the distance between them indicating how close the analogy is. Forms of visual attention, for example, have the most overlap and are the most directly comparable across biology and machine learning. Some forms of attention, such as overall arousal, don't have an obvious artificial analogue.

4.1. How to Enhance Performance

There are two components to the study of how attention works in the brain that can be considered flip sides of the same coin. The first is the question of how attention enhances performance in the way that it does—that is, how do the neural changes associated with attention make the brain better at performing tasks. The second is how and why attention is deployed in the way that it is—what factors lead to the selection of certain items or tasks for attention and not others.

Neuroscientists have spent a lot of time investigating the former question. In large part, the applicability of these findings to artificial neural systems, however, may not be straightforward. Multiplicative scaling of activity appears in both biological and artificial systems and is an effective means of implementing attention. However, many of the observed effects of attention in the brain make sense mainly as a means of increasing the signal carried by noisy, spiking neurons. This includes increased synchronization across neurons and decreased firing variability. Without analogs for these changes in deep neural networks, it is hard to take inspiration from them. What's more, the training procedures for neural networks can automatically determine the changes in activity needed to enhance performance on a well-defined task and so lessons from biological changes may not be as relevant.

On the other hand, the observation that attention can impact spiking-specific features such as action potential height, burstiness, and precise spike times may indicate the usefulness of spiking networks. Specifically, spiking models offer more degrees of freedom for attention to control and thus allow attention to possibly have larger and/or more nuanced impacts.

Looking at the anatomy of attention may provide usable insights to people designing architectures for artificial systems. For example, visual attention appears to modulate activity more strongly in later visual areas like V4 ( Noudoost et al., 2010 ), whereas auditory attention can modulate activity much earlier in the processing stream. The level at which attention should act could thus be a relevant architectural variable. In this vein, recent work has shown that removing self-attention from the early layers of a Transformer model enhances its performance on certain natural language processing tasks and also makes the model a better predictor of human fMRI signals during language processing ( Toneva and Wehbe, 2019 ).

The existence of cross-modal cueing—wherein attention cued in one sensory modality can cause attention to be deployed to the same object or location in another modality—indicates some amount of direct interaction between different sensory systems. Whereas many multi-modal models in machine learning use entirely separate processing streams that are only combined at the end, allowing some horizontal connections between different input streams may help coordinate their processing.

Attention also interacts with the kind of adaptation that normally occurs in sensory processing. Generally, neural network models do not have mechanisms for adaptation—that is, neurons have no means of reducing their activity if given the same input for multiple time steps. Given that adaptation helps make changes and anomalies stand out, it may be useful to include. In a model with adaption, attention mechanisms should work to reactivate adapted neurons if the repeated stimulus is deemed important.

Finally, some forms of attention appear to act in multiple ways on the same system. For example, visual attention is believed to both: (1) enhance the sensitivity of visual neurons in the cortex by modulating their activity and (2) change subcortical activity such that sensory information is readout differently ( Birman and Gardner, 2019 ; Sreenivasan and Sridharan, 2019 ). In this way, attention uses two different mechanisms, in different parts of the brain, to create its effect. Allowing attention to modulate multiple components of a model architecture in complementary ways may allow it to have more robust and effective impacts.

4.2. How to Deploy Attention

The question of how to deploy attention is likely the more relevant challenge for producing complex and integrated artificial intelligence. Choosing the relevant information in a stream of incoming stimuli, picking the best task to engage in, or deciding whether to engage in anything at all requires that an agent have an integrative understanding of its state, environment, and needs.

The most direct way to take influence from biological attention is to mimic it directly. Scanpath models, for example, have existed in the study of saliency for many years. They attempt to predict the series of fixations that humans make while viewing images ( Borji and Itti, 2019 ). A more direct approach to training attention was used in Linsley et al. (2018) . Here, a large dataset of human top-down attention was collected by having subjects label the regions of images most relevant for object classification. The task-specific saliency maps created through this method were used to train attention in a deep convolutional neural network whose main task was object recognition. They found that influencing the activity of intermediate layers with this method could increase performance. Another way of learning a teacher's saliency map was given in Zagoruyko and Komodakis (2016) .

Combined training on tasks and neural data collected from human visual areas has also helped the performance of CNNs ( Fong et al., 2018 ). Using neural data collected during attention tasks in particular could help train attention models. Such transfer could also be done for other tasks. For example, tracking eye movements during reading could inform NLP models; thus far, eye movements have been used to help train a part-of-speech tagging model ( Barrett et al., 2016 ). Interestingly, infants may learn from attending to what adults around them attend to and the coordination of attention more broadly across agents may be very helpful in a social species. Therefore, the attention of others should influence how attention is guided. Attempts to coordinate joint attention will need to be integrated into attention systems ( Kaplan and Hafner, 2006 ; Klein et al., 2009 ).

Activities would likely need to flexibly decide which of several possible goals should be achieved at any time and therefore where attention should be placed. This problem clearly interacts closely with issues around reinforcement learning—particularly hierarchical reinforcement learning which involves the choosing of subtasks—as such decisions must be based on expected positive or negative outcomes. Indeed, there is a close relationship between attention and reward as previously rewarded stimuli attract attention even in contexts where they no longer provide reward ( Camara et al., 2013 ). A better understanding of how humans choose which tasks to engage in and when should allow human behavior to inform the design of a multi-task AI.

To this end, the theory put forth in Shenhav et al. (2013) , which says that allocation of the brain's limited ability to control different processes is based on the expected value of that control, may be of use. In this framework, the dorsal anterior cingulate cortex is responsible for integrating diverse information—including the cognitive costs of control—in order to calculate the expected value of control and thus direct processes like attention. Another approach for understanding human executive control in complex tasks is inverse reinforcement learning. This method was recently applied to a dataset of eye movements during visual search in order to determine the reward functions and policies used by humans ( Zelinsky et al., 2020 ).

An additional factor that drives biological attention but is perhaps underrepresented in artificial attention systems is curiosity ( Gottlieb et al., 2013 ). In biology, novel, confusing, and surprising stimuli can grab attention, and inferotemporal and perirhinal cortex are believed to signal novel visual situations via an adaptation mechanism that reduces responses to familiar inputs. Reinforcement learning algorithms that include novelty as part of the estimate of the value of a state can encourage this kind of exploration ( Jaegle et al., 2019 ). How exactly to calculate surprise or novelty in different circumstances is not always clear, however. Previous work on biological attention has understood attention selection in Bayesian terms of surprise or information gathering and these framings may be useful for artificial systems ( Itti and Baldi, 2006 ; Mirza et al., 2019 ).

A final issue in the selection of attention is how conflicts are resolved. Given the brain's multiple forms of attention—arousal, bottom-up, top-down, etc.—how do conflicts regarding the appropriate locus of attention get settled? Looking at the visual system, it seems that the local circuits that these multiple systems target are burdened with this task. These circuits receive neuromodulatory input along with top-down signals which they must integrate with the bottom-up input driving their activity. Horizontal connections mediate this competition, potentially using winner-take-all mechanisms. This can be mimicked in the architecture of artificial systems.

4.3. Attention and Learning

Attention, through its role in determining what enters memory, guides learning. Most artificial systems with attention include the attention mechanism throughout training. In this way, the attention mechanism is trained along with the base architecture; however, with the exception of the Neural Turing Machine, the model does not continue learning once the functioning attention system is in place. Therefore, the ability of attention to control learning and memory is still not explicitly considered in these systems.

Attention could help make efficient use of data by directing learning to the relevant components and relationships in the input. For example, saliency maps have been used as part of the pre-processing for various computer vision tasks ( Lee et al., 2004 ; Wolf et al., 2007 ; Bai and Wang, 2014 ). Focusing subsequent processing only on regions that are intrinsically salient can prevent wasteful processing on irrelevant regions and, in the context of network training, could also prevent overfitting to these regions. Using saliency maps in this way, however, requires a definition of saliency that works for the problem at hand. Using the features of images that capture bottom-up attention in humans has worked for some computer vision problems; looking at human data in other modalities may be useful as well.

In a related vein, studies on infants suggest that they have priors that guide their attention to relevant stimuli such as faces. Using such priors could bootstrap learning both of how to process important stimuli and how to better attend to their relevant features ( Johnson, 2001 ).

In addition to deciding which portions of the data to process, top-down attention can also be thought of as selecting which elements of the network should be most engaged during processing. Insofar as learning will occur most strongly in the parts of the network that are most engaged, this is another means by which attention guides learning. Constraining the number of parameters that will be updated in response to any given input is an effective form of regularization, as can be seen in the use of dropout and batch normalization. Attention—rather than randomly choosing which units to engage and disengage—is constrained to choose units that will also help performance on this task. It is therefore a more task-specific form of regularization.

In this way, attention may be particularly helpful for continual learning where the aim is to update a network to perform better on a specific task while not disrupting performance on the other tasks the network has already learned to do. A related concept, conditional computation, has recently been applied to the problem of continual learning ( Lin et al., 2019 ). In conditional computation, the parameters of a network are a function of the current input (it can thus be thought of as an extreme form of the type of modulation done by attention); optimizing the network for efficient continual learning involves controlling the amount of interference between different inputs. More generically, it may be helpful to think of attention, in part, as a means of guarding against undesirable synaptic changes.

Attention and learning also work in a loop. Specifically, attention guides what is learned about the world and internal world models are used to guide attention. This inter-dependency has recently been formalized in terms of a reinforcement learning framework that also incorporates cognitive Bayesian inference models that have succeeded in explaining human learning and decision making ( Radulescu et al., 2019 ). Interconnections between basal ganglia and prefrontal cortex are believed to support the interplay between reinforcement learning and attention selection.

At a more abstract level, the mere presence of attention in the brain's architecture can influence representation learning. The global workspace theory of consciousness says that at any moment a limited amount of information selected from the brain's activity can enter working memory and be available for further joint processing ( Baars, 2005 ). Inspired by this, the ‘consciousness prior' in machine learning emphasizes a neural network architecture with a low-dimensional representation that arises from attention applied to an underlying high-dimensional state representation ( Bengio, 2017 ). This low-D representation should efficiently represent the world at an abstract level such that it can be used to summarize and make predictions about future states. The presence of this attention-mediated bottleneck has a trickle-down effect that encourages disentangled representations at all levels such that they can be flexibly combined to guide actions and make predictions.

Conscious attention is required for the learning of many complex skills such as playing a musical instrument. However once fully learned, these processes can become automatic, possibly freeing attention up to focus on other things ( Treisman et al., 1992 ). The mechanisms of this transformation are not entirely clear but insofar as they seem to rely on moving the burden of the task to different, possibly lower/more reflexive brain areas, it may benefit artificial systems to have multiple redundant pathways that can be engaged differently by attention ( Poldrack et al., 2005 ).

4.4. Limitations of Attention: Bugs or Features?

Biological attention does not work perfectly. As mentioned above, performance can suffer when switching between different kinds of attention, arousal levels need be just right in order to reach peak performance, and top-down attention can be interrupted by irrelevant but salient stimuli. A question when transferring attention to artificial systems is are these limitations bugs to be avoided or features to be incorporated?

Distractability, in general, seems like a feature of attention rather than a bug. Even when attempting to focus on a task it is beneficial to still be aware of—and distractable by—potentially life-threatening changes in the environment. The problem comes only when an agent is overly distractable to inputs that do not pose a threat or provide relevant information. Thus, artificial systems should balance the strength of top down attention such that it still allows for the processing of unexpected but informative stimuli. For example, attentional blink refers to the phenomenon wherein a subject misses a second target in a stream of targets and distractors if it occurs quickly after a first target ( Shapiro et al., 1997 ). While this makes performance worse, it may be necessary to give the brain time to process and act on the first target. In this way, it prevents distractability to ensure follow through.

Any agent, artificial or biological, will have some limitations on its energy resources. Therefore, prudent decisions about when to engage in the world versus enter an energy-saving state such as sleep will always be of relevance. For many animals sleep occurs according to a schedule but, as was discussed, it can also be delayed or interrupted by attention-demanding situations. The decision about when to enter a sleep state must thus be made based on a cost-benefit analysis of what can be gained by staying awake. Because sleep is also known to consolidate memories and perform other vital tasks beyond just energy conservation, this decision may be a complex one. Artificial systems will need to have an integrative understanding of their current state and future demands to make this decision.

5. Conclusions

Attention is a large and complex topic that sprawls across psychology, neuroscience, and artificial intelligence. While many of the topics studied under this name are non-overlapping in their mechanisms, they do share a core theme of the flexible control of limited resources. General findings about flexibility and wise uses of resources can help guide the development of AI, as can specific findings about the best means of deploying attention to specific sensory modalities or tasks.

Author Contributions

GL conceived and wrote the article and generated the figures.

This work was supported by a Marie Skłodowska-Curie Individual Fellowship (No. 844003) and a Sainsbury Wellcome Centre/Gatsby Computational Unit Fellowship.

Conflict of Interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The reviewer MR declared a past co-authorship with the author GL to the handling Editor.

Acknowledgments

The author would like to thank Jacqueline Gottlieb and the three reviewers for their insights and pointers to references.

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Keywords: attention, artificial neural networks, machine learning, vision, memory, awareness

Citation: Lindsay GW (2020) Attention in Psychology, Neuroscience, and Machine Learning. Front. Comput. Neurosci. 14:29. doi: 10.3389/fncom.2020.00029

Received: 02 December 2019; Accepted: 23 April 2020; Published: 16 April 2020.

Reviewed by:

Copyright © 2020 Lindsay. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Grace W. Lindsay, gracewlindsay@gmail.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Psychology Discussion

Top 2 experiments on attention | experimental psychology.

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List of top two psychological experiments on attention!

Experiment # 1. Span of Attention – Visual:

At any given moment there are several stimuli in the environment competing for our attention. However, our sense organs can respond to only a limited number of them at the same time. This limit is known as span of attention. The span varies from individual to individual, from sense organ to sense organ, and also according to the nature of the stimuli. The earliest psychologist to be interested in the problem was Sir William Hamilton, who made a very crude experimental attempt to study the problem.

An advance was made on Hamilton’s method by Jevons, the logician. However, real scientific experimental work on the problem was started by J.M. Cattell, who used the tachistoscope for this experiment. After Cattell, a number of experimenters have studied the span of attention under different conditions. Later experimenters have distinguished between span of attention and span of apprehension and also found that span of apprehension is greater than span of attention.

To determine the span of attention for the following type of Visual stimuli:

1. Single dots

2. Grouped dots

3. Nonsense Syllables (meaningless combinations of letters)

4. Meaningful words, and

5. Numbers.

Materials Required :

A tachistoscope of the falling-door type, exposure cards having the following materials printed on them:

(i) Each set of 3 Cards bearing 3 to 10 single dots. (i.e., 3 cards with 3 dots, 3 cards with 4 dots, etc.). The dots are to be arranged in different patterns.

(ii) Cards with 3 dots in each group ranging from 3 groups to 10 groups (having 3 dots corresponding to a single dot in the earlier set of cards.

(iii) Cards with nonsense syllables having 3 letter syllables to 10 letter syllables, 3 different combinations of letters at each level (3 cards with 3 letter syllables, 3 cards with 4 letter syllables etc.).

(iv) Cards with meaningful words, again ranging from 3 letter words to 12 letter words (different words at each stage).

(v) Cards with numbers ranging from 3 digit numbers to 10 digit numbers (again 3 cards at each stage with 3 different numbers having the same number of digits).

Description of the Apparatus:

There are different types of tachistoscopes. Falling-door type is the one which usually has a fixed exposure time. There are other tachistoscopes which are operated electrically and the exposure is variable and adjustable (camera- shutter types). For the present experiment, the simple falling-door type is adequate.

It consists of a wooden screen with a window in the middle which is covered by a movable falling shutter. This shutter can be closed or opened with the help of a lever which is behind the screen at the top. The exposure time is usually 1/10th of a second. It is sufficient to enable the subject to take a quick glance at the exposed material and at the same time short enough to prevent him from reading or memorising it.

The subject is seated in front of the tachistoscope such that he has a good view of the window. The experimenter sits on the other side of the apparatus keeping the five separate sets of cards with him. The sets are shuffled separately and kept ready for the experiment. The experiment has to be done separately for each of the five sets.

First Set of Cards:

Instructions to the Subject :

“Observe this window carefully. I will say ‘ready’ and open this window. You will see a card with a number of dots. Try to find out how many dots are there. The card will be exposed only for a short time”.

The experimenter then shuffles the set of cards with single dots and exposes them one after the other, each time giving the ‘ready’ signal. After presenting each card, he makes a note of the actual number of dots as well as the subject’s response. The complete set is exposed once and then exposed for a second time. The subject thus views each card twice and therefore there are 6 stimuli for each level, i.e., 6 exposures for 3 dots, 6 exposures for 4 dots, etc.

After exposing all the cards the experimenter finds out how many times the subject has responded correctly for each level out of the possible 6 times.

Tabulate the results as follows:

Now we are ready to determine the span. For experimental purposes the span can be defined as the maximum number of dots to which at least 75 per cent of correct responses are made, viz. if a subject responds 100 per cent correctly to three dots, 83.3 per cent to four dots and 66.67 per cent to five dots, his span lies between 4 and 5. The span can now be determined by interpolation between 4 and 5.

Procedure with the Other Sets of Cards:

The procedure for the other sets is essentially the same excepting for the instructions, which are as following:

i. Instructions for Groups of Dots:

“This time instead of single dots you will see small separate groups of 3 dots each. After seeing each card, tell me how many groups of dots are there in each card”.

ii. Instructions for Nonsense Syllables :

“In this series you will see some syllables instead of dots. After seeing each card, write down the syllables as correctly as possible.”

iii. Instructions for Meaningful Words :

“Here on each card you will find a familiar and meaningful word. Try to write down the word you see on each card.”

iv. Instructions for Numbers :

“In this set, instead of words or dots you will find numbers; as before you will have to write down the number you see”.

After exposing all the sets determine the span in each case as illustrated in the case of dots. The whole experiment can be done in two sessions. Otherwise the subject is likely to get bored and fatigued.

(1) Study individual variations in the span for the different types.

(2) Compare the Spans:

There will be some interesting findings with respect to the differences in the attention spans between single dots and groups of dots. The subject who has a span of 6 single dots may also have a span of 6 for groups of dots though the latter actually includes 18 dots. This is because of the factor of grouping. Each group of dots is responded to as a single stimulus, because of the factor of organisation.

Similarly the span for meaningful words will be usually much higher than the span for nonsense syllables, though both are made up of same number of letters of alphabet. This is because of the factor of meaning and familiarity. In the case of meaningful words and numbers there is apprehension or understanding in addition to mere attention. Furthermore, the factor of familiarity is helpful.

Application:

This experiment has a number of practical applications. A very common illustration is the registration numbers given to automobiles. Usually, automobile numbers do not exceed four digits. This is because the traffic constable would be unable to note down the registration number of automobiles violating traffic rules if the number exceeds four digits. However, the letters of alphabet before the numbers are perceived because they are grouped separately.

Experiment # 2. Distraction of Attention :

When we are attending to some stimulus or work, any noise or other type of disturbance tends to affect the efficiency of our attention. This phenomenon of irrelevant stimuli interfering with our attentive process is called ‘distraction’. Not all stimuli can distract out attention, viz., the ticking of a table clock on our study table does not ordinarily disturb us. Sometimes even strong stimuli do not disturb us when we are prepared for it.

One experiment showed that students working on some problems could, to a large extent, resist distractions of different types by putting in more effort. Baker employing dance music as distractor found that in many instances, the subject did better when music was played. Morgan in his classical experiments proved that subjects can soon get used to a distracting influence, and that often efficiency is lost when distracting influence is removed.

Introspective reports, however, show that subjects feel a greater strain and have to put in greater effort under distracting conditions to maintain the same level of efficiency of attention. Experiments on distraction are usually carried out as group experiments.

To determine the effect of extraneous and irrelevant stimuli on the work efficiency.

Material Required :

A long list of arithmetic problems of uniform difficulty, a sound proof room fitted with number of buzzers, bells, bright lights, etc., to serve as visual and auditory distractions.

Procedure :

The experiment is done under four conditions:

1. Controlled condition.

2. Auditory distraction.

3. Visual distraction.

4. Combination of visual and auditory distraction.

The experiment can be conducted by adopting any one of the following experimental designs:

Experimental Design 1:

Different groups of subjects are assigned to the four conditions.

Experimental Design 2:

The performance of all the subjects under controlled conditions, without any kind of deliberate distraction, is assessed and on the basis of these scores, the subjects are grouped into three matched groups. Each one of these groups is assigned to each one of the three conditions of distraction.

Experimental Design 3:

The performance of each subject is assessed under all the four conditions.

In the first experimental design, the subjects are selected and assigned to the four conditions by following the method of randomisation.

In the second experimental design, the subjects are categorised into three matched groups by following any one of the techniques of matching the groups, and each one of these groups is assigned to one experimental condition by following the method of randomisation.

In the third experimental design the subjects are categorised into four groups by following the method of randomisation and the performance of each one of these groups under all the four conditions is observed. However, the order of presentation of the four conditions should be counter-balanced.

Instructions to the Subjects :

Give the selected arithmetic problems to the subjects and ask them to solve them.

1. Controlled Condition :

For five minutes allow them to solve the problems under normal conditions, and then ask them to highlight the last problem they have solved.

2. Auditory Distraction :

Suddenly, at the end of 5 minutes, switch on the buzzers and the bells so that the room is filled with loud noises. The subjects have to continue solving the problems. Ask the subjects to indicate the last problem they have solved.

3. Visual Distraction :

At the end of five minutes switch off the buzzers but switch on the bright lights, flashing glaring lights of different colours and ask the subjects to mark the last problem they have solved.

4. Combination of Visual and Auditory Distraction :

At the end of five minutes, switch on both the buzzers and the lights and ask the subjects to highlight the last problem solved.

Now collect the answer sheets and correct them. Tabulate the number of problems attempted and the number correctly solved for each of the five-minute periods. Take the introspective report of the subject.

Compare the results under the four conditions. See whether work efficiency has, been affected. Analyse the introspective reports to find out the subjects inner reactions to various distractions. Also find out whether they had to put in greater effort to carry out the work under different conditions of distraction.

Tabulate group results as follows:

1. Calculate the Mean & SD under all the conditions for problems attempted as well as problems correctly solved.

2. Do all subjects show the same type of change under distraction?

3. Which condition is most distracting for the group and which the least?

4. Do all the subjects show the same trend of performance under all the four conditions?

It may be interesting to study the effect of preparedness of the subject for distraction.

Instruct the subjects and give them prior information about the occurrence of the distraction. This can be done by giving the instructions for all the conditions at the beginning or specifically before the start of each session studying the effect of a specified condition.

Applications :

Such experiments are useful in pinpointing factors that distract workers in factories, offices, etc. where the efficiency of the workers can be improved by eliminating the distracting conditions. Industrial psychologists have carried out several experiments on this subject. It has been found that minimisation of noise in the work situation facilitates the employees to concentrate better on their tasks resulting in better output. Further, excess of noise has also been found to lead to stress.

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Experiments , Experimental Psychology , Attention , Experiments on Attention

What Is the Cognitive Psychology Approach? 12 Key Theories

Maintaining focus on the oncoming traffic is paramount, yet I am barely aware of the seagulls flying overhead.

These noisy birds only receive attention when I am safely walking up the other side of the road, their cries reminding me of childhood seaside vacations.

Cognitive psychology focuses on the internal mental processes needed to make sense of the environment and decide on the next appropriate action (Eysenck & Keane, 2015).

This article explores the cognitive psychology approach, its origins, and several theories and models involved in cognition.

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This Article Contains:

What is the cognitive psychology approach, a brief history of cognitive psychology, cognitive psychology vs behaviorism, 12 key theories, concepts, and models, fascinating research experiments, a look at positive cognitive psychology, interesting resources from positivepsychology.com, a take-home message.

The upsurge of research into the mysteries of the human brain and mind has been considerable in recent decades, with recognition of the importance of cognitive process in clinical psychology and social psychology (Eysenck & Keane, 2015).

As a result, cognitive psychology has profoundly affected the field of psychology and our understanding of what it is to be human.

Perhaps more surprisingly, it has had such an effect without clear boundaries, an integrated set of assumptions and concepts, or a recognizable spokesperson (Gross, 2020).

So, what exactly is the cognitive psychology approach?

Cognitive psychology attempts to understand human cognition by focusing on what appear to be cognitive tasks that require little effort (Goldstein, 2011).

Let’s return to our example of walking down the road. Imagine now that we are also taking a call. We’re now combining several concurrent cognitive tasks:

• Perceiving the environment Distinguishing cars from traffic signals and discerning their direction and speed on the road as well as the people ahead standing, talking, and blocking the sidewalk.
• Paying attention Attending to what our partner is asking us on the phone, above the traffic noise.
• Visualizing Forming a mental image of items in the house, responding to the question, “Where did you leave your car keys?”
• Comprehending and producing language Understanding the real question (“I need to take the car. Where are your keys?”) from what is said and formulating a suitable reply.
• Problem-solving Working out how to get to the next appointment without the car.
• Decision-making Concluding that the timing of one meeting will not work and choosing to push it to another day.

While cognitive psychologists initially focused firmly on an analogy comparing the mind to a computer, their understanding has moved on.

There are currently four approaches, often overlapping and frequently combined, that science uses to understand human cognition (Eysenck & Keane, 2015):

• Cognitive psychology The attempt to “understand human cognition by using behavioral evidence” (Eysenck & Keane, 2015, p. 2).
• Cognitive neuropsychology Understanding ‘normal’ cognition through the study of patients living with a brain injury.
• Cognitive neuroscience Combining evidence from the brain with behavior to form a more complete picture of cognition.
• Computational cognitive science Using computational models to understand and test our understanding of human cognition.

Cognitive psychology plays a massive and essential role in understanding human cognition and is stronger because of its close relationships and interdependencies with other academic disciplines (Eysenck & Keane, 2015).

In 1868, a Dutch physiologist, Franciscus Donders, began to measure reaction time – something we would now see as an experiment in cognitive psychology (Goldstein, 2011).

Donders recognized that mental responses could not be measured directly but could be inferred from behavior. Not long after, Hermann Ebbinghaus began examining the nature and inner workings of human memory using nonsense syllables (Goldstein, 2011).

By the late 1800s, Wilhelm Wundt had set up the first laboratory dedicated to studying the mind scientifically. His approach became known as structuralism . His bold aim was to build a periodic table of the mind , containing all the sensations involved in creating any experience (Goldstein, 2011).

However, the use of analytical introspection to uncover hidden mental processes was gradually dropped when John Watson proposed a new psychological approach that became known as behaviorism (Goldstein, 2011).

Watson rejected the introspective approach and instead focused on observable behavior. His idea of classical conditioning – the connection of a new stimulus with a previously neutral one – was later surpassed by B. F. Skinner’s idea of operant conditioning , which focused on positive reinforcement (Goldstein, 2011).

Both theories sought to understand the relationship between stimulus and response rather than the mind’s inner workings (Goldstein, 2011).

Prompted by a scathing attack by linguist and cognitive scientist Noam Chomsky, by the 1950s behaviorism as the dominant psychological discipline was in decline. The introduction of the digital computer led to the information-processing approach , inspiring psychologists to think of the mind in terms of a sequence of processing stages (Goldstein, 2011).

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Moore (1996) recognized the tensions of the paradigm shift from behaviorism to cognitive psychology.

While research into cognitive psychology, cognitive neuropsychology, cognitive neuroscience, and computational cognitive science is now widely accepted as the driving force behind understanding mental processes (such as memory, perception, problem-solving, and attention), this was not always the case (Gross, 2020).

Moore (1996) highlighted the relationship between behaviorism and the relatively new field of cognitive psychology, and the sometimes mistaken assumptions regarding the nature of the former approach:

• Behaviorism is typically only associated with studying publicly observable behavior. Unlike behaviorism, cognitive psychology is viewed as free of the restrictions of logical positivism, which rely on verification through observation.

Since then, modern cognitive psychology has incorporated findings from many other disciplines, including evolutionary psychology, computer science, artificial intelligence , and neuroscience (Eysenck & Keane, 2015).

• Unlike behaviorism, cognitive psychology is theoretical and explanatory. Behaviorism is often considered merely descriptive, while cognitive psychology is seen as being able to explain what is behind behavior.

Particular ongoing advances in cognitive psychology include perception, language comprehension and production, and problem-solving (Eysenck & Keane, 2015).

• Behaviorism cannot incorporate theoretical terms. While challenged by some behaviorists at the time, it was argued that behaviorism could not incorporate theoretical terms unless related to directly observable behavior.

At the time, cognitive psychologists also argued that it was wrong of behaviorists to interpret mental states in terms of brain states.

Neuroscience advances, such as new imaging techniques like functional MRI, continue to offer fresh insights into the relationship between the brain and mental states (Eysenck & Keane, 2015).

Clearly, the relationship between behaviorism and the developing field of cognitive psychology has been complex. However, cognitive psychology has grown into a school of thought that has led to significant advances in understanding cognition, especially when teamed up with other developments in computing and neuroscience.

This may not have been possible without the shift in the dominant schools of thought in psychology (Gross, 2020; Goldstein, 2011; Eysenck & Keane, 2015).

And while it is beyond the scope of this article to cover the full breadth or depth of the areas of research, we list several of the most important and fascinating specialties and theories below.

It is hardly possible to imagine a world in which attention doesn’t play an essential role in how we interact with the environment, and yet, we rarely give it a thought.

According to cognitive psychology, attention is most active when driven by an individual’s expectations or goals, known as top-down processing . On the other hand, it is more passive when controlled by external stimuli, such as a loud noise, referred to as bottom-up processing (Eysenck & Keane, 2015).

A further distinction exists between focused attention (selective) and divided attention . Research into the former explores how we are able to focus on one item (noise, image, etc.) when there are several. In contrast, the latter looks at how we can maintain attention on two or more stimuli simultaneously.

Donald Broadbent proposed the bottleneck model to explain how we can attend to just one message when several are presented, for example, in dichotic listening experiments, where different auditory stimuli are presented to each ear. Broadbent’s model suggests multiple processing stages, each one progressively restricting the information flow (Goldstein, 2011).

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As with all other areas of cognition, perception is far more complicated than we might first imagine. Take, for example, vision. While a great deal of research has “involved presenting a visual stimulus and assessing aspects of its processing,” there is also the time aspect to consider (Eysenck & Keane, 2015, p. 121).

We need to not only perceive objects, but also make sense of their movement and detect changes in the visual environment over time (Eysenck & Keane, 2015).

Research suggests perception, like attention, combines bottom-up and top-down processing. Bottom-up processing involves neurons that fire in response to specific elements of an image – perhaps aspects of a face, nose, eyebrows, jawline, etc. Top-down processing considers how the knowledge someone brings with them affects their perception.

Bottom-down processing helps explain why two people, presented with the same stimuli, experience different perceptions as a result of their expectations and prior knowledge (Goldstein, 2011).

Combining bottom-up and top-down processing also enables the individual to make sense of both static and moving images when limited information is available; we can track a person walking through a crowd or a plane disappearing in and out of clouds (Eysenck & Keane, 2015).

The mirror neuron system is incredibly fascinating and is proving valuable in our attempts to understand biological motion. Observing actions activates similar areas of the brain as performing them. The model appears to explain how we can imitate the actions of another person – crucial to learning (Eysenck & Keane, 2015).

Language comprehension

Whether written or spoken, understanding language involves a high degree of multi-level processing (Eysenck & Keane, 2015).

Comprehension begins with an initial analysis of sentence structure (larger language units require additional processing). Beyond processing syntax (the rules for building and analyzing sentences), analysis of sentence meaning ( semantics ) is necessary to understand if the interpretation should be literal or involve irony, metaphor, or sarcasm (Eysenck & Keane, 2015).

Pragmatics examines intended meaning. For example, shouting, “That’s the doorbell!” is not likely to be a simple observation, but rather a request to answer the door (Eysenck & Keane, 2015).

Several models have been proposed to understand the analysis and comprehension of sentences, known as parsing , including (Eysenck & Keane, 2015):

• Garden-path model This model attempts to explain why some sentences are ambiguous (such as, “The horse raced past the barn fell.”). It suggests they are challenging to comprehend because the analysis is performed on each individual unit of the sentence with little feedback, and correction is inhibited.
• Constraint-based model The interpretations of a sentence may be limited by several constraints, including syntactic, semantic, and general world knowledge.
• Unrestricted race model This model combines the garden-path and constraint-based model, and suggests all sources of information inform syntactic structure. One such interpretation is selected until it is discarded, with good reason, for another.
• Good-enough representation This model proposes that parsing provides a ‘good-enough’ interpretation rather than something detailed, accurate, and complete.

The research and theories above hint at the vast complexity of human cognition and explain why so many models and concepts attempt to answer what happens when it works and, equally important, when it doesn’t.

A level of psychology: the cognitive approach – Atomi

There are many research experiments in cognitive psychology that highlight the successes and failings of human cognition. Each of the following three offers insight into the mental processes behind our thinking and behavior.

Cocktail party phenomenon

Selective attention – or in this case, selective listening – is often exemplified by what has become known as the cocktail party phenomenon  (Eysenck & Keane, 2015).

Even in a busy room and possibly mid-conversation, we can often hear if someone else mentions our name. It seems we can filter out surrounding noise by combining bottom-up and top-down processing to create a “winner takes it all” situation where the processing of one high-value auditory input suppresses the brain activity of all others (Goldstein, 2011).

While people may believe that the speed of hand movement allows magicians to trick us, research suggests the main factor is misdirection (Eysenck & Keane, 2015).

A 2010 study of a trick involving the disappearance of a lighter identified that when the lighter was dropped (to hide it from a later hand-opening finale), it was masked by directing attention from the fixation point – known as covert attention – with surprising effectiveness.

However, subjects were able to identify the drop when their attention was directed to the fixation point – known as overt attention (Kuhn & Findlay, 2010).

In a thought-provoking study exploring freewill, participants were asked to consciously decide whether to move their finger left or right while a functional MRI scanner monitored their prefrontal cortex and parietal cortex (Soon, Brass, Heinze, & Haynes, 2008).

Brain activity predicted the direction of movement a full seven seconds before they consciously became aware of their decision. While follow-up research has challenged some of the findings, it appears that brain activity may come before conscious thinking (Eysenck & Keane, 2015).

Associations have been found between positive emotions, creative thinking, and overall wellbeing, suggesting environmental changes that may benefit staff productivity and innovation in the workplace (Yuan, 2015).

Factors explored include creating climates geared toward creativity, boosting challenge, trust, freedom, risk taking, low conflict, and even the beneficial effects of humor.

Undoubtedly, further innovation will be seen from marrying the two powerful and compelling new fields of positive psychology and cognitive psychology.

17 Top-Rated Positive Psychology Exercises for Practitioners

Expand your arsenal and impact with these 17 Positive Psychology Exercises [PDF] , scientifically designed to promote human flourishing, meaning, and wellbeing.

Created by Experts. 100% Science-based.

We have many tools, worksheets, and exercises to explore and improve attention, problem-solving, and the ability to regulate emotions.

Why not download our free emotional intelligence pack and try out the powerful tools contained within?

• Building Emotional Awareness In this exercise, we foster emotional intelligence by mindfully attending to existing emotional states.
• Identifying False Beliefs About Emotions Our beliefs often operate outside of conscious awareness. This exercise addresses clients’ basic and often unconscious assumptions about their emotions.

Other free resources include:

• Skills for Regulating Emotion We can learn to manage our emotions by focusing on more positive experiences than negative ones.
• Emotional Repetition and Attention Remodeling Identifying phrases used to describe ourselves can help desensitize negative feelings .

More extensive versions of the following tools are available with a subscription to the Positive Psychology Toolkit© , but here is a brief overview:

• Creating Savoring Rituals It’s possible to increase positive emotions by sharpening our sensory perceptions via savoring.

Learning to focus can help.

Step one – Identify everyday activities that bring you pleasure. Step two – Focus on experiencing pleasure as it happens when doing these activities.

At the end of the week, take some time to record your reflections on creating savoring rituals.

• Extracting Strengths From Problems Surprisingly, using our strengths too much can harm our problem-solving ability.

In this exercise, we examine an existing issue in a client’s life:

Step one – Describe a current problem. Step two – Identify the problematic context or life domain. Step three – Identify the problematic behavior in yourself. Step four – Recognize your underlying strength. Step five – Identify what you can do to remedy the problem.

If you’re looking for more science-based ways to help others enhance their wellbeing, check out this signature collection of 17 validated positive psychology tools for practitioners. Use them to help others flourish and thrive.

Cognitive psychology is crucial in our search for understanding how we interact with and make sense of a constantly changing and potentially harmful environment.

Not only that, it offers insight into what happens when things go wrong and the likely impact on our wellbeing and ability to cope with life events.

Cognitive psychology’s strength is its willingness to embrace research findings from many other disciplines, combining them with existing psychological theory to create new models of cognition.

The tasks we appear to carry out unconsciously are a great deal more complex than they might first appear. Perception, attention, problem-solving, language comprehension and production, and decision-making often happen without intentional thought and yet have enormous consequences on our lives.

Use this article as a starting point to explore the many and diverse aspects of cognitive psychology. Consider their relationships with associated research fields and reflect on the importance of understanding cognition in helping clients overcome complex events or circumstances.

We hope you enjoyed reading this article. Don’t forget to download our three Positive Psychology Exercises for free .

• Eysenck, M. W., & Keane, M. T. (2015). Cognitive psychology: A student’s handbook . Psychology Press.
• Goldstein, E. B. (2011). Cognitive psychology . Wadsworth, Cengage Learning.
• Gross, R. D. (2020). Psychology: The science of mind and behaviour . Hodder and Stoughton.
• Kuhn, G., & Findlay, J. M. (2010). Misdirection, attention and awareness: Inattentional blindness reveals temporal relationship between eye movements and visual awareness. The Quarterly Journal of Experimental Psychology , 63 (1), 136–146.
• Moore, J. (1996). On the relation between behaviorism and cognitive psychology. Journal of Mind and Behavior , 17 (4), 345–367
• Soon, C. S., Brass, M., Heinze, H., & Haynes, J. (2008). Unconscious determinants of free decisions in the human brain. Nature Neuroscience , 11 (5), 543–545.
• Yuan, L. (2015). The happier one is, the more creative one becomes: An investigation on inspirational positive emotions from both subjective well-being and satisfaction at work. Psychology , 6 , 201–209.

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What our readers think.

As a widowed Mother and Grandmother, whom was recently told by an adult child that maybe I should have “cognitive” testing done, I found this article to be very informative and refreshing. Having the ability to read and and learn about cognitive psychology is interesting as their are so many ways our brains are affected from the time we are born until the time we reach each and every stage in life. I have spent time with my grandchildren who are from age 19 months, through 15 years old , and spend time with children who are 35, 34, and 32, and my parents who are 88 and 84. I appreciate your article and your time in writing it. Sincerely,

Cognitive Psychology creates & build human capacity to push physical and mental limits. My concept of cognition in human behavior was judged by the most time I met my lawyer or the doctor. Most of the time while listening a pause, oh I see and it is perpetual transition to see. Cognition emergence is very vital support as we see & perceive. My practices in engineering solution are base on my cognitive sensibilities.You article provokes the same perceptions. Thank you

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