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1 Levels of Processing: Development of an Idea

• Published: June 2021
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This chapter focuses on the background and development of the levels of processing (LOP) ideas proposed by Craik and Lockhart (1972) and on the empirical support for the ideas provided by Craik and Tulving (1975). The chapter describes how the concept arose from the British work on models of attention by Donald Broadbent and Anne Treisman in the 1960s; specifically on how the concept of depth of processing grafts a framework for memory research on to Treisman’s hierarchical model of selective attention. After a brief survey of empirical work, the chapter deals with criticisms and rebuttals of the LOP ideas and findings, its relations to other ideas such as transfer–appropriate processing and to some more recent extensions.

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Levels of Processing Theory (Craik & Lockhart, 1972)

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.

Learn about our Editorial Process

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.

On This Page:

The levels of processing model (Craik & Lockhart, 1972) focuses on the depth of processing involved in memory, and predicts the deeper information is processed, the longer a memory trace will last.

Craik defined depth as:

“the meaningfulness extracted from the stimulus rather than in terms of the number of analyses performed upon it.” (1973, p. 48)

Unlike the multi-store model , it is a non-structured approach. The basic idea is that memory is just what happens as a result of processing information.

Memory is just a by-product of the depth of information processing, and there is no clear distinction between short-term and long-term memory.

Therefore, instead of concentrating on the stores/structures involved (i.e. short-term  memory & long-term memory ), this theory concentrates on the processes involved in memory.

Levels of processing: The idea that the way information is encoded affects how well it is remembered.  The deeper the level of processing, the easier the information is to recall.

We can process information in 3 ways:

Shallow Processing

– This takes two forms

1 . Structural processing (appearance) which is when we encode only the physical qualities of something.  E.g. the typeface of a word or how the letters look. 2 . Phonemic processing – which is when we encode its sound.

Shallow processing only involves maintenance rehearsal (repetition to help us hold something in the STM) and leads to fairly short-term retention of information.

This is the only rehearsal type to occur within the multi-store model.

Deep Processing

3 . Semantic processing , which happens when we encode the meaning of a word and relate it to similar words with similar meaning.

Deep processing involves elaboration rehearsal which involves a more meaningful analysis (e.g. images, thinking, associations, etc.) of information and leads to better recall.

For example, giving words a meaning or linking them with previous knowledge.

Key Study: Craik and Tulving (1975)

To investigate how deep and shallow processing affects memory recall.

Participants were presented with a series of 60 words about which they had to answer one of three questions.  Some questions required the participants to process the word in a deep way (e.g. semantic) and others in a shallow way (e.g. structural and phonemic). For example:

• Structural / visual processing: ‘Is the word in capital letters or small letters?
• Phonemic / auditory processing: ‘Does the word rhyme with . . .?’
• Semantic processing: ‘Does the word go in this sentence . . . . ?

Participants were then given a long list of 180 words into which the original words had been mixed. They were asked to pick out the original words.

Participants recalled more words that were semantically processed compared to phonemically and visually processed words.

Semantically processed words involve elaboration rehearsal and deep processing which results in more accurate recall.  Phonemic and visually processed words involve shallow processing and less accurate recall.

Real-Life Applications

This explanation of memory is useful in everyday life because it highlights the way in which elaboration, which requires deeper processing of information, can aid memory. Three examples of this are.

• Reworking – putting information in your own words or talking about it with someone else.
• Method of loci – when trying to remember a list of items, linking each with a familiar place or route.
• Imagery – by creating an image of something you want to remember, you elaborate on it and encode it visually (i.e. a mind map).

The above examples could all be used to revise psychology using semantic processing (e.g. explaining memory models to your mum, using mind maps etc.) and should result in deeper processing through using elaboration rehearsal .

Consequently, more information will be remembered (and recalled) and better exam results should be achieved.

The theory is an improvement on Atkinson & Shiffrin’s account of transfer from STM to LTM . For example, elaboration rehearsal leads to recall of information than just maintenance rehearsal.

The levels of processing model changed the direction of memory research. It showed that encoding was not a simple, straightforward process. This widened the focus from seeing long-term memory as a simple storage unit to seeing it as a complex processing system.

Craik and Lockhart’s ideas led to hundreds of experiments, most of which confirmed the superiourity of “deep” semantic processing for remembering information. It explains why we remember some things much better and for much longer than others.

This explanation of memory is useful in everyday life because it highlights the way in which elaboration, which requires deeper processing of information, can aid memory.

Despite these strengths, there are a number of criticisms of the levels of processing theory:

• It does not explain how the deeper processing results in better memories.
• Deeper processing takes more effort than shallow processing and it could be this, rather than the depth of processing that makes it more likely people will remember something.
• The concept of depth is vague and cannot be observed. Therefore, it cannot be objectively measured.

Eysenck (1990) claims that the levels of processing theory describes rather than explains. Craik and Lockhart (1972) argued that deep processing leads to better long-term memory than shallow processing. However, they failed to provide a detailed account of why deep processing is so effective.

However, recent studies have clarified this point – it appears that deeper coding produces better retention because it is more elaborate. Elaborative encoding enriches the memory representation of an item by activating many aspects of its meaning and linking it into the pre-existing network of semantic associations.

Later research indicated that processing is more complex and varied than the levels of processing theory suggests. In other words, there is more to processing than depth and elaboration.

For example, research by Bransford et al. (1979) indicated that a sentence such as, “A mosquito is like a doctor because both draw blood” is more likely to be recalled than the more elaborated sentence, “A mosquito is like a racoon because they both have head, legs and jaws”.

It appears that it is the distinctiveness of the first sentence which makes it easier to remember – it’s unusual to compare a doctor to a mosquito. As a result, the sentence stands out and is more easily recalled.

Another problem is that participants typically spend a longer time processing the deeper or more difficult tasks. So, it could be that the results are partly due to more time being spent on the material.

The type of processing, the amount of effort & the length of time spent on processing tend to be confounded. Deeper processing goes with more effort and more time, so it is difficult to know which factor influences the results.

The ideas of “depth” and “elaboration” are vague and ill defined (Eysenck, 1978).  As a result, they are difficult to measure. Indeed, there is no independent way of measuring the depth of processing.  This can lead to a circular argument – it is predicted that deeply processed information will be remembered better, but the measure of depth of processing is how well the information is remembered.

The levels of processing theory focuses on the processes involved in memory, and thus ignores the structures. There is evidence to support the idea of memory structures such as STM and LTM as the Multi-Store Model proposed (e.g. H.M., serial position effect etc.). Therefore, memory is more complex than described by the LOP theory.

Bransford, J. D., Franks, J. J., Morris, C.D., & Stein, B.S.(1979). Some general constraints on learning and memory research. In L.S. Cermak & F.I.M. Craik(Eds.), Levels of processing in human memory (pp.331–354). Hillsdale, NJ: Lawrence Erlbaum AssociatesInc.

Craik, F. I. M., & Lockhart, R. S. (1972). Levels of processing: A framework for memory research. Journal of Verbal Learning and Verbal behavior, 11, 671-684.

Craik, F.I.M., & Tulving, E. (1975). Depth of processing and the retention of words in episodic memory. Journal of Experimental Psychology: General , 104, 268-294.

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

Further Information

What is the main idea of levels of processing theory.

The main idea of the levels of processing theory is that the depth at which information is processed during encoding affects its subsequent recall. According to this theory, information processed at a deeper level, such as through semantic or meaningful processing, is more likely to be remembered than information processed at a shallow level, such as through superficial or sensory-based processing.

What is deep processing?

Deep processing refers to the meaningful and thorough encoding of information. It involves engaging with the content thoughtfully and elaborately, making connections to existing knowledge and personal experiences. Deep processing promotes better memory retention and recall than shallow, surface-level processing.

How does shallow processing affect encoding?

Shallow processing involves superficial or surface-level encoding of information. It typically focuses on sensory features or basic characteristics without engaging in meaningful analysis or elaboration. As a result, shallow processing leads to poorer memory encoding and weaker retention than deep processing, which involves more thorough and meaningful engagement with the information.

• DOI: 10.25215/0203.030
• Corpus ID: 212509893

Levels of Processing

• Dipal Patel , Mehul Jadav , Fr. Sanil Mathew Sj
• Published 25 June 2015

18 Citations

Mechanisms behind the testing effect: an empirical investigation of retrieval practice in meaningful learning, long-term spacing effect benefits in developmental amnesia: case experiments in rehabilitation., the importance of unitization for familiarity-based learning., representational shifts made visible: movement away from the prototype in memory for hue, self-controlled feedback is effective if it is based on the learner’s performance: a replication and extension of chiviacowsky and wulf (2005), prior perceptual processing enhances the effect of emotional arousal on the neural correlates of memory retrieval, affective processing requires awareness., differential effects of acute cortisol administration on deep and shallow episodic memory traces: a study on healthy males, vl: a further case of erroneous recollection, re-engaging with the past: recapitulation of encoding operations during episodic retrieval, 6 references, depth of processing and the retention of words.

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New sensor can detect cholesterol and glucose from skin

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A new, non-invasive method may do away with the need to draw blood for testing blood glucose and cholesterol levels.

Researchers from the National University of Singapore (NUS) and the Agency for Science, Technology and Research (A*Star) have developed a stretchable, hydrogel-based sensor that can detect such biomarkers in a solid state on the skin.

The technology could be used in wearables for purposes such as chronic disease management and remote patient monitoring.

The team’s findings were published in the scientific journal Nature Materials in June.

While traditional methods of monitoring biomarkers in fluids such as blood, urine and sweat are effective, they come with hurdles that can impede the early diagnosis and treatment of diseases.

Blood tests, for example, can be invasive and inconvenient, while sweat can be difficult to induce in inactive people.

NUSSU wants to address issue of tourists on campus

Related stories, three artists dropped from teaching courses at nus, ibm, nus to set up new ai research and innovation centre, nus student with cerebral palsy graduates with honours.

The researchers noted that such challenges do not apply to solid-state epidermal biomarkers – which include cholesterol and lactate – that are found in the stratum corneum, the outermost layer of the skin.

Such biomarkers have shown strong correlations with diseases such as cardiovascular disease and diabetes. Outside of some scattered literature, these correlations have been overlooked for decades, said Assistant Professor Liu Yuxin from the NUS Institute for Health Innovation & Technology.

Prof Liu, who is one of the leads for the study, said solid electrodes placed on the skin, used in conventional monitors, do not allow for the electrochemical sensing of these biomarkers.

Instead of electrodes, the sensor developed by the researchers uses hydrogels, which dissolve and diffuse solid-state epidermal biomarkers that then undergo electrochemical reactions catalysed by enzymes.

This results in the transfer of electrons to an electronically conductive hydrogel, which can then be read by a flexible printed circuit board connected to the sensor.

The circuit board is able to wirelessly transmit the relevant physiological data to a user, who can use the data to monitor a patient’s health.

Dr Yang Le, principal scientist and head of the sensors and flexible electronics department at the A*Star Institute of Materials Research and Engineering, said the sensor is possibly the first such device that is able to monitor biomarkers on dry skin.

“The stretchable design enhances comfort and accuracy as well, by adapting to our skin’s natural elasticity. This innovation can change the way we approach health and lifestyle monitoring, particularly for those living with chronic conditions requiring constant health monitoring,” said Dr Yang, who is the study’s other lead.

Clinical studies showed that the sensor found “strong correlations” between biomarkers on the skin and those in blood samples, positing that it could act as an alternative to blood tests for monitoring chronic diseases such as diabetes and cardiovascular conditions.

It is also able to detect solid-state lactate and cholesterol, even at very low levels.

High levels of lactate, also known as lactic acid, can be indicative of a number of diseases, such as pulmonary or circulatory disorders, and liver disease.

One possible application of the technology is as a replacement for the pregnancy diabetic test, said Prof Liu.

The test for gestational diabetes – which affects about one in five pregnant women in Singapore – is offered to all women who are between 24 weeks and 28 weeks of pregnancy here.

“Rather than subject pregnant women to multiple blood draws, our sensor could be used to track real-time sugar levels conveniently in patients’ homes, with a similar level of accuracy as traditional tests. This also can be applied to diabetes in general, replacing the need for regular finger-prick tests,” Prof Liu said.

Dr Yang identified the daily monitoring of heart health as another possible use of the innovation.

“The research team has embarked on a research programme to work closely with cardiologists in establishing clinical correlation between biomarkers – lactate, cholesterol and glucose – with heart health,” she said.

While the sensor is currently limited to these three biomarkers, the researchers aim to expand its abilities to detect others as well, she added.

Noting that the sensor can eventually be integrated with a variety of devices, including smartwatches and fitness trackers, Dr Yang said the technology can be employed both for lifestyle and medical purposes.

The researchers expect to be able to commercialise the sensor within the next five years or so, once the technology is more mature, she added.

Medical wearables are becoming increasingly popular, with market researcher Fortune Business Insights predicting that the global market for such devices is expected to grow from $120 billion in 2024 to $427 billion by 2032.

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Research on 3D convolutional autoencoder enhanced metro abnormal behavior detection based on multi-level attentional memory

• Published: 17 August 2024

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• Run Ye   ORCID: orcid.org/0000-0001-9376-5122 1 , 2 ,
• Kun Zhang 1 ,
• Cheng Zhang 2 ,
• Bin Yan 1 , 2 &
• Xiaojia Zhou 1 , 2  

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Subway is one of the most important rail transit tools in China, which has the advantages of convenience, safety and high efficiency, and subway has also become the main means of transportation for people to travel. However, the subway scene has the characteristics of narrow space and large passenger flow, and abnormal behavior events often occur in the subway scene during passenger rush hours. At present, the abnormal behavior detection methods of manual monitoring in subway scenes have been unable to meet the increasing demand of passenger traffic. In this paper, pedestrian abnormal behavior detection in subway scene is studied, and a self-encoder abnormal behavior detection method based on channel attention mechanism and multi-level memory enhancement is proposed. It solves the problems of excessive generalization ability of traditional convolutional self-encoders and difficulties in extracting pedestrian behavior features under complex subway background, which is verified by experiments. Finally, better performance has been achieved in UCSDPed2 dataset, CUHK Avenue dataset and Chengdu Metro self-built dataset.

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This work was partially supported by National Natural Science Foundation of China under Grant (No. 61973055), and Fundamental Research Funds for the Central Universities (No. ZYGX2020J011).

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Ye, R., Zhang, K., Zhang, C. et al. Research on 3D convolutional autoencoder enhanced metro abnormal behavior detection based on multi-level attentional memory. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-20061-w

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Received : 12 July 2023

Revised : 05 August 2024

Accepted : 08 August 2024

Published : 17 August 2024

DOI : https://doi.org/10.1007/s11042-024-20061-w

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