ebbinghaus forgetting curve experiment

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List

Replication and Analysis of Ebbinghaus’ Forgetting Curve

Jaap m. j. murre.

University of Amsterdam, Amsterdam, The Netherlands

Conceived and designed the experiments: JMJM JD. Performed the experiments: JD. Analyzed the data: JMJM JD. Contributed reagents/materials/analysis tools: JMJM JD. Wrote the paper: JMJM JD.

Associated Data

Data are available from the Open Science Framework at DOI: osf.io/6kfrp .

We present a successful replication of Ebbinghaus’ classic forgetting curve from 1880 based on the method of savings. One subject spent 70 hours learning lists and relearning them after 20 min, 1 hour, 9 hours, 1 day, 2 days, or 31 days. The results are similar to Ebbinghaus' original data. We analyze the effects of serial position on forgetting and investigate what mathematical equations present a good fit to the Ebbinghaus forgetting curve and its replications. We conclude that the Ebbinghaus forgetting curve has indeed been replicated and that it is not completely smooth but most probably shows a jump upwards starting at the 24 hour data point.

Introduction

This paper describes a replication of one of the most important early experiments in psychology, namely Ebbinghaus' classic experiment on forgetting from 1880 and 1885. We replicated the experiment that yielded the famous forgetting curve describing forgetting over intervals ranging from 20 minutes to 31 days. Ebbinghaus' goal was to find the lawful relation between retention and time-since-learning. This is why he fitted the data to two different functions (a power function, 1880, and a logarithmic function, 1885), as have many theorists since (e.g., [ 1 , 2 – 4 ]). This papers also includes an analysis—including one with a new model—of the shape of the Ebbinghaus' forgetting curve and its replications. Do the replicated forgetting curves have the same shape, or must we conclude that Ebbinghaus' forgetting curve was idiosyncratic and that quite different shapes may occur?

There is currently an increasing interest in replication studies in psychology, motivated by a growing uneasiness in the community about unreliable findings in psychology. It seems particularly important to try to replicate classic studies that are included in every textbook on cognitive psychology and may also be known by the general public. A good example of this is the classic study by Bartlett [ 5 ], which until 1999 had only had unsuccessful replication attempts, until finally Bergman and Roediger [ 6 ] succeeded in replicating the basic findings. One of the reasons earlier replications may have failed is because not all details were well-documented in the original study from 1932. The exact instructions, for example, were not included. This may explain why Wynn and Logie [ 7 ] had found the forgetting gradient in their experiment to be quite different from the one in Bartlett's experiment. Bergman and Roediger [ 6 ] also argue that this may have been caused by certain differences in the study design. Replication of classic experiments, thus, serves the dual purpose of verifying the reliability of the original results and uncovering more precisely how the original experiment was conducted.

It is hard to overestimate the importance of Hermann Ebbinghaus' contribution to experimental psychology. Influenced by the work of the German philosopher Herbart, he was the first to carry out a series of rigorous experiments on the shape of forgetting, which he completed in 1880. The experiment itself was preceded by a period in which he tried out a variety of materials and methods. After having tested himself with tones, numbers, and poem stanzas, he decided that none of these served his purposes. Tones were too cumbersome to handle and too difficult to reproduce for him, he did not find digits zero to nine suitable as basic units for the long-running experiments he envisioned, and the poem fragments he tried to learn (from Byron’s Don Juan) were deemed too variable in the meanings they evoked and therefore likely to cause measurement error [ 8 ] (p. 14–17). He, therefore, introduced nonsense syllables, which had more uniform characteristics than existing words or other verbal material. In his later experiments on learning, however, he did verify his results with the Don Juan verses, confirming both his main results on learning and his intuition that the latter stimuli did indeed yield much more variance in the data [ 9 ]. Since his introduction of nonsense syllables, a large number of experiments in experimental psychology has been based on highly controlled, artificial stimuli.

In all experiments reported by Ebbinghaus [ 9 ], he used only himself as a subject. Single-subject designs are not unusual in memory psychology. Especially in the study of autobiographical memory we find several diary studies based on one person’s personal memories (e.g., [ 10 , 11 , 12 ]). They have the advantage that there is no inter-subject variability, although they still require hundreds of trials to reduce the variance due to differences in stimuli and other factors. This places a great burden on the subject. Indeed, Ebbinghaus’ forgetting curve is based on seven months of experimenting, often up to three sessions per day. Wagenaar [ 13 ] meticulously recorded one daily memory during six years and spent several months recalling these.

A disadvantage of a single-subject design is that it remains unclear what the shape of forgetting would be with other subjects. Are the results universal or did the subject happen to have a memory that was exceptional in some way [ 14 , 15 ]. The generality of the results can be assessed with a faithful replication. There have been a number of—mostly early—replications of Ebbinghaus’s forgetting curve, notably by Radossawljewitsch [ 16 ] and Finkenbinder [ 17 ], but these authors used a much slower presentation rate of the stimuli of 2 s per stimulus, where Ebbinghaus learned at 0.4 s per stimulus. This was partially the result of the development of devices for mechanical presentation by Müller and colleagues [ 18 , 19 ], who presented materials at a rate one stimulus per second. Slowing down the presentation this much alters the nature of the processing with more time to generate meaningful associations to otherwise meaningless syllables. Though the resulting forgetting curves are clearly of interest to the field, we feel that the slow method of presentation form a large departure from Ebbinghaus’ original study. Also, Finkenbinder’s [ 17 ] longest retention interval is 3 days, instead of 31 days and though in the experiment by Radossawljewitsch [ 16 ] the retention interval range extends up to 120 days, his design suffers from an uneven distribution of intervals throughout time and time-of-day. Stimuli were learned in order: in the first few days of the study all 5 min intervals were learned, then the 20 min intervals, and so on. Because he did not use a pre-experimental practice phase, the early intervals took longer to learn while the subjects were still getting used to the materials and the procedure; it is likely that this has affected the shape of the forgetting curve reported by him. There are other differences between these two studies and Ebbinghaus’, for example, the degree to which was learned and whether the subjects were allowed to pause between lists.

There are several unanswered questions about Ebbinghaus’ results that formed part of the motivation for us to undertake this replication. His basic stimulus was a ‘row’ of thirteen nonsense syllables, which he studied until he could correctly recall it in the correct order twice in succession. A question that seems pertinent is how stimuli at different serial positions were learned and how these were forgotten over time. Another question is how his measure of choice, namely savings (see below) is related to the nowadays more common measure of percentage correct. Finally, we were interested in the role of interference or fatigue in the course of the experiment.

To help answer these questions, we consulted not only the widely published text of 1885 [ 9 ], which was translated into English in 1913 [ 20 ], but also an earlier report of 1880 [ 8 ]. This is a handwritten manuscript that he submitted for his Habilitation , which in Germany is a requirement to be considered for a full professorship. This text (the so called Urmanuscript or original manuscript) has been typeset and republished in German in 1983. Even with this additional source, however, we still could not answer the questions above.

For these reasons, we decided to replicate Ebbinghaus’ forgetting experiment. If our replication yielded similar results, this would support the generality of Ebbinghaus’ curve and through a more detailed analysis of our data, we would be able to address the issues above. In the course of preparing for our study, we found that there has been at least one other replication study, namely by Heller, Mack, and Seitz [ 21 ]. This study has been published only in German, without an English abstract, and is not easily accessible; at the time of writing, it is not available in electronic format (i.e., it is not available online) and it has never been cited in international journals in English. It is, however, a thorough study and an excellent replication attempt. Where the Ebbinghaus [ 8 , 9 ] texts are unclear about certain details, we have mostly followed Heller et al. [ 21 ] as a guideline so that we can also compare our results with theirs. Because we feel this is an important study that has not received the readership it deserves, we will mention more of its details here than we would have had it been more accessible at this point in time.

In 1885, Ebbinghaus introduces the savings measure of learning and memory (it does not appear in this form in his earlier text from 1880). Savings is defined as the relative amount of time saved on the second learning trial as a result of having had the first. Suppose, one has to repeat a list for 25 times in order to reach twice perfect recollection and that after one day, one needs 20 repetitions to relearn it. This is 5 less than the original 25; we can say that on relearning we saved 20% with respect to the original 25 rehearsals (5/25 = 0.2 or 20%). If it takes just as long to relearn the list as it took to learn it originally, then savings is 0. If the list is still completely known at the second trial (i.e., no forgetting at all), then savings is 1 or 100%. Ebbinghaus prefers to express savings in terms of time spent learning and relearning but the principle remains the same. After Ebbinghaus’ publication in 1885, the savings measure remained popular for several decades [ 16 – 19 , 22 – 24 ]. Eventually, researchers found the savings method too unreliable compared with other methods of measuring memory [ 24 ] and in the following decades it was used much less with some exceptions (e.g., [ 25 ]). Later, an important improvement was suggested [ 26 , 27 ], where learning is not to the 100% criterion but to a much lower one, such as 50% correct. These improved versions of the method are used nowadays, for example, when studying forgetting of foreign languages [ 28 – 30 ].

In the following, we will first report our replication experiment. Then, in the Discussion section we will revisit the shape of forgetting, analyze the effects of serial position on forgetting, and investigate what mathematical equations present a good fit to the Ebbinghaus forgetting curve and its replications. Finally, we will study whether there is evidence for a jump at 24 hours in these curves, which some authors have attributed to the effect of sleep.

The Replication Experiment

The current study was set up to replicate the findings by Ebbinghaus [ 8 , 9 ]. Despite a quite detailed account of his experiment, we found some information to be lacking and we had to estimate or guess these details, as outlined below. Also, we did not have the seven months available that Ebbinghaus invested in the experiment, but we had to accommodate our design to a 75 day period. We nonetheless believe that our experiment is close enough to his to be still called a replication.

There were a few differences between Ebbinghaus’ study, Heller et al.’s [ 21 ] replication, and ours. (i) Because we were limited in time, like Heller et al. [ 21 ], we ran only 10 replications per time interval, instead of the 12 to 45 by Ebbinghaus. This means the variance in our data is larger than in Ebbinghaus’ especially at the longest time intervals; apart from that no systematic differences were introduced. (ii) We were not able to experiment at a fixed time of day. Ebbinghaus (1880), who started experimenting in the morning at (A) 10:00, and then sometimes also at (B) 12:00 and usually at (C) 19:00 to 20:00, noticed that there was a difference between these times of the day in his ability to acquire a list. He subtracted 5% for B and 13% for C from the learning times at these hours to normalize the data with respect to time A. Heller et al. [ 21 ] were also able to conduct experimental sessions at specific times throughout the day, but they did not find such a time-of-day effect and hence did not implement a correction. (iii) Our stimulus material conformed to the phonotactics of the Dutch language and thus differs from both Ebbinghaus and Heller et al. Also, in contrast to both Ebbinghaus and Heller et al. we removed syllables that had too much meaning in order to further balance the level of difficulty of the stimuli. (iv) Our subject, J. Dros, was younger than H. Ebbinghaus, who was 29 during his experiments in 1879–1880. The ages of the two subjects in Heller et al. [ 21 ] are not given. (v) Ebbinghaus [ 8 ] gives exact testing dates for each of the short time-intervals but not for the longer ones (24 hours and up). Hence, we do not know exactly when he learned and relearned the lists for the longer intervals. This makes it impossible to calculate the number of interfering lists between learning and relearning. It also makes it nearly certain that our schedule differed from his (and from that of Heller et al. [ 21 ] who also do not supply such a schedule).

The second author, J. Dros, (22 years, male) was the only subject in the experiment. This experiment was reviewed and approved by the Review Board of the Psychology Department of the University of Amsterdam (see www.lab.uva.nl ). The project is filed with case number 2014-BC-3879 (contact is Dr. R.H. Phaf). Consent was implicit as the second author of the paper was also the only subject on which we report. This was also approved by the Review Board. The subject's native language is Dutch, making this the first non-German replication of Ebbinghaus’ forgetting experiment.

The learning material consisted of 70 lists. Each list consisted of 104 nonsense syllables, which in turn consisted of 8 ‘rows’ of 13 syllables.

Nonsense syllables

Each syllable consisted of 3 or 4 lower-case letters. The structure of a syllable was a lower-case consonant-vowel-consonant (CVC) structure. The consonant of the syllable was always one of b, d, f, g, h, j, k, l, m, n, p, r, s, t, or w. The vowel could be one of e, i, o, u, aa, uu, ee, ei, eu, oe, ie, oo or ui. The double-letters stand for standard Dutch vowels. The last consonant of the syllable was one of f, g, k, l, m, n, p, r, s, or t.

The number of different possible consonant-vowel-consonant combinations on the basis of these letter combinations is 2100 (15 × 14 × 10). Not every possible consonant-vowel-consonant combination was included in the learning material; we removed words that had too much meaning in Dutch, in order to further balance the difficulty of the stimuli. Syllables with meanings in other languages spoken by the subject, such as English and German, were not excluded.

Row and list construction

Using the pseudo-random generator of Excel 2010, rows of 13 syllables were constructed. Within a row we did not allow two syllables with the same vowel in direct succession. We also did not allow two identical syllables within one row, but we did allow them in different rows within a single list. When syllables needed to be adjusted we first tried changing only the first or second letter of a syllable until the criteria were met. If this did not suffice, additional letters were changed. The adjustment process was not purely random but was carried out by hand during stimulus preparation by the authors.

The only independent variable in this experiment was the time-interval, which started at the end of learning a list for the first time. The time-interval ended at the beginning of learning a list for the second time. The time-intervals between learning and relearning were the same as Ebbinghaus [ 8 ]: 20 minutes, 1 hour, 9 hours, 1 day, 2 days, 6 days and 31 days. For each time interval, 10 lists were learned and relearned (for the 9 hour interval only 9 lists were learned due to unforeseen circumstances).

We need to elaborate on the choice of these time intervals as there is some confusion about the exact length of the shorter retention intervals used by Ebbinghaus. He mentions both 15 min (in 1880 [ 8 ]) and 19 min (in 1885 [ 9 ]) for the shortest interval, and 63 min and 8.75 hours (525 min) for the longer intervals. He also states that relearning took place “after about one third of an hour, after 1 hour, after 9 hours, one day, two days, six days, or 31 days.” ([ 20 ], p. 66). Heller et al. [ 21 ] followed the latter intervals, using 20 min, 60 min and 9 hours, etc. We have also used these, given that this seems to have been the intended lengths of Ebbinghaus’ retention intervals. The deviations these values by Ebbinghaus are based on corrections after the experiment.

Ebbinghaus’ shortest interval (‘20 minutes’) is based on almost immediately relearning a list of eight rows and hence the interval depends on how long it took to learn the eight lists. When relearning the lists so soon it takes much less time to relearning them, than the original learning, so that the intervals between learning and relearning of lists is not constant, with List 8 being relearned earliest (e.g., 20 min) and List 1 latest (e.g., 10 min). Ebbinghaus [ 8 ] (p. 50) states that the average time is about 15 min and argues that that whereas he does not know exactly how to correct for these variable learning times, the error will be small. We recalculated the average of the times stated on page 51 of the 1880 text and find it to be 1010 s or 16.8 min. Ebbinghaus keeps using the value of 15 min throughout his text from 1880, including for fitting his ‘power function’ equation (see below). The learning and relearning times given in his 1885/1913 volume [ 20 ] are the same as in 1880 [ 8 ], but to each interval he has now added 88 s for reasons that are not made clear. The average of these learning intervals then becomes 18.3 min. Given that he also remarks that for the shortest interval “relearning of the first series of a test followed almost immediately or after an interval of one or two minutes upon the learning of the last series of the same test” ([ 20 ], p. 66) may explain rounding 18.3 min to 19 min, the value used throughout the text from 1885. In general, it seems he made more or less intuitive corrections for the variable learning times and changed his mind from 1880 to 1885 about the most appropriate method to approach this. We have used 19 min, 63 min, and 8.75 hours in the graphs and tables (and fits), for Ebbinghaus’ data. For the other data sets, we use 20 min, 1 hour, and 9 hours.

Measurement of repetitions and time

The main measurement was the number of repetitions needed to correctly reproduce the syllables in a row in the correct order.

For the forgetting curve experiment, Ebbinghaus [ 8 ] learned until twice correct, but in later experiments switched to once correct [ 9 ], because he found there to be no essential difference in the outcomes. We chose to also learn to once correct. Heller et al. [ 21 ] (p. 8) seem to be using the once-correct criterion as well but this is not made entirely clear.

Ebbinghaus [ 8 ] uses elapsed time to calculate the number of repetitions, because he finds keeping count too distracting. Heller et al. [ 21 ] use a chain with wooden, colored beads, much like a rosary to keep count. We found that word processing software (Microsoft Word) was handy to keep track of the number of repetitions. During learning, each single repetition of a row was counted by pressing the button ‘1’ on the keyboard at the beginning of every single repetition of a row. At the end of learning a list, the total number of 1’s for each row was counted and entered into the database.

We also measured the time in seconds needed to learn a list. A clock was shown on the computer screen during the task and we recorded the begin times and end times of learning a list. Subtracted from the time were the pauses of 15 s between two rows (cf. [ 8 ], p. 19). When relearning a list, the extra time (15 s) introduced by the voice recording of the first retrieval attempt was subtracted from the total relearning time.

The practice phase and experimental phase

Following Ebbinghaus, we preceded the experimental phase of the experiment with a practice phase to prevent as much as possible general learning effects due to growing experience with the task and materials. The practice phase took place between 08-11-2011 and 29-11-2011. A total of 14 lists was learned and relearned after 20 minutes (Heller et al. [ 21 ] relearned lists after one hour). After these, a further 19 lists were learned only (i.e., not relearned later) for additional familiarization with the task.

In the experimental phase, which took place between 01-12-2011 and 13-02-2012, a total of 69 lists was learned and relearned (9 for the 9 hour interval). The total time spent on data collection in the experimental phase amounted to about 70 hours. We distributed the ten lists for each time interval as much as possible over the whole experimental period. Due to the limited time available to run the whole experiment, we were not able to achieve this for the 31 days time interval condition, so that we decided to learn these lists near the beginning of this experimental period.

List learning phase

All lists were printed on paper (black ink, font ‘Calibri’, 11 points) (eight rows per page; a row was actually printed in a column format for easier studying). The non-studied rows were covered by sheets of paper. The subject was seated behind a desk in a quiet room. The main goal was to learn a list as quickly as possible, to learn each row until it could be reproduced correctly once.

Following Ebbinghaus [ 8 ] (p.18), the syllables were softly spoken from the first syllable to the 13 th syllable at a constant speed of 150 beats per minute. The repetition of a row took 5.2 s on average. Our subject preferred to speak the syllables in a jambus-like manner, where syllables were paired so that the emphasis always was on the second syllable (i.e. wes-hóm, niem-hág, etc…). The last syllable (13 th syllable) was not paired to another syllable and was not spoken with an emphasis. Here, we use an approach similar to Heller et al. [ 21 ] and not to Ebbinghaus, who prefers a ¾ rhythm, stressing the first syllable in each group of three (this is in fact the reason he gives for his preference for rows of length 10, 13, or 16 syllables, see Ebbinghaus [ 8 ], p. 19).

During the learning phase, the subject had a continuous choice to either read or reproduce the syllables. Towards the end of the learning process, occasional attempts were made to produce an entire row by heart. When there was a moment of hesitation during such ‘blind’ reproduction, the rest of the list was read (i.e., not blind) to the end. Blind reproduction always started with the first syllable of a row. Rows were not learned in parts. Each time the 13 th syllable had been reached and the row still contained errors, it was read again from the beginning. After having learned one row and before starting the next one, there was an interval of 15 sec. The interval served as a moment of rest and pause. All of this was aimed at following Ebbinghaus as closely as possible. Each row was thus learned to a 100% correct criterion before moving to learning the next row.

During the pre-pretraining phase, a metronome was used at first to achieve a recitation rate of 150 beats, but this was found to be too intrusive and distracting. Eventually, the rhythm was internalized and the metronome was only used for occasional rate checks. During the experimental phase, it was not used. After each rehearsal of a row, there was a little transition-pause of about 3 beats to take a breath before the next repetition of the row.

Learning of a list was considered complete, if all rows had thus been learned in order. The retention interval was started at the time a list had been learned. On most days two or three lists were learned or relearned with a maximum of four. The full learning schedule is given in Fig 1 .

Relearning times are not shown but can be derived by adding the retention interval (e.g., 6 days).

Relearning phase

We added one additional measurement to Ebbinghaus’ procedure: the number of correct syllables at first reproduction of a row at relearning, not necessarily at the right location. We recorded the recall of the first time a row was relearned with an Olympus WS-450s voice-recorder. After the last relearning session of the experimental phase, the sound files were transcribed and scored.

Rows were relearned in the same order as during original learning. During relearning, the subject was seated at a desk with a computer. A word processing program was opened on the computer and a clock was visible on the screen. A sheet of paper with a list printed on it was laid in front of the subject with only the syllables from the row to be learned visible. The other rows were covered by a piece of paper. During learning, the subject used the ‘1’ button on the keyboard to count the number of repetitions. Following Ebbinghaus [ 8 ] (p. 19), after successful relearning there was a 15 second pause. Then, the row learned last was covered, a next row uncovered and the procedure was repeated.

Relearning of a row started with turning on the voice recorder. Then the row was read once as described above. Twenty seconds after turning the voice recorder on, the subject stopped recall attempts and turned the voice recorder off. After that, relearning continued in the usual fashion. This procedure was repeated for every row in the list. After a list had been relearned, the audio file of the recording was saved on a computer.

The forgetting curve

The main objective was to replicate Ebbinghaus’ famous forgetting curve. The average number of repetitions is given in Table 1 and the number of seconds spent on learning and relearning each list, with the calculated savings scores, is given in Table 2 . The raw data of this experiment are freely available online at the website of the Open Science Foundation (URL: https://osf.io/6kfrp/ ). To see whether there were differences between the time-intervals in the average number of repetitions at first learning, a one-way independent ANOVA with the average number of repetitions per list as the dependent variable and the time-interval as the independent variable. There was no significant effect for the time-interval, F(6, 69) = 0.691, p = 0.658. This means that the average number of repetitions per list did not differ significantly per time interval, indicating there were no randomization confounds.

Savings scores (based on time in s) are compared with those of Ebbinghaus [ 8 ], and Mack and Seitz [ 21 ] in Table 3 and plotted with error bars in Fig 2 using loglog coordinates and in Fig 3 using log coordinates only for the time axis. Despite the fact that the original experiment dates from 1880 and replications were done over a century later, and despite the fact that our replication was carried out in a Dutch language context, the four forgetting curves share many characteristics. To facilitate a direct comparison we have overlaid the four curves in Fig 4 where we have normalized the savings scores such that the first data point (at 20 min) was always equal to 1.0. Given expected individual differences, we find the resemblance of the four graphs remarkable. The greatest deviation is by Dros at 31 days; his savings score is much lower that any of the other three. We can only speculate at the reason for this. It may be that this subject simply has more long-term forgetting. Another explanation bears on the fact that the lists for the 31 day interval were all learned early in the experiment (see Fig 1 ) and learning times were shorter at the beginning, due to greater initial enthusiasm, less pro-active interference, or yet another reason. It is also possible that the low savings score on the 31 day point is an effect of the relatively short time period in which initial learning took place for the 31-day data point (i.e., massed learning); learning for other intervals was more widely spaced. We were forced to place the learning sessions at the beginning because of the limited time available by the subject for data collection. Ebbinghaus could spread his sessions of a seven-month interval, though we do not know the exact schedule for the intervals past 9 hours, nor do we know anything about the schedule followed by Mack and Seitz. Finally, we considered where the number of intervening lists had influenced retention at the 31-day-point. For this time point, the number of intervening lists varied from 23 to 33. Our analysis, however, revealed virtually no relationship with savings score as a function of intervening lists ( R 2 was about 8.4%). Our data did not allow us to further disentangle the effect of number of intervening lists versus time because list number and time were too strongly confounded in our design.

We found a gradual increase in learning time throughout the course of the experiment as can be seen in Fig 5 , where averaged learning time in s has been plotted for consecutive ten-day periods (‘bins’). In the course of the 75 days of the experimental phase there was an average increase in learning time of 2.67 s per day for a list (this linear regression explained 56.18% of the variance). If we correct for this steady increase, which mostly affects the 31 day interval, the corrected savings measure would be 0.137 for the 31 day interval instead of 0.0410. This, however, is still well below the values for the three others, which are in the 0.20 range. This steady increase in learning time may be due to pro-active interference or fatigue. Ebbinghaus [ 8 ] and Heller et al. [ 21 ] do not report or analyze this.

We also analyzed the false alarms and correct answers measured on first relearning. We did a one-way independent ANOVA with the number of false alarms per row as the dependent variable and the retention interval as the independent variable. There was no significant effect for the time interval, F(6 , 512) = 0 . 753 , p > 0 . 608 , indicating that the number of false alarms was not significantly different for the time-intervals. A one-way independent ANOVA with the number of hits per row as the dependent variable and retention interval as the independent variable, however, yielded a significant effect for the time-interval, F(6 , 512) = 3 . 85 , p < 0 . 01 , which we will analyze further in the next section.

Serial position effects

In Fig 6 , we have plotted the average serial position curves for each retention interval and for the grand average. Even if a correct syllable was not mentioned at the correct position, it was still scored as correct for its intended position (this was rare and had only a small effect on the data). Fig 6 shows clear serial position curves for all retention intervals.

In Fig 6 , it also seems that there is more forgetting with time in the middle positions. In Fig 7A , overall forgetting is shown, where the average of all positions is shown for each retention interval. Though there is forgetting, these curves are much shallower than the savings curves, which are shown as well for comparison. In Fig 7B , forgetting is shown for four groups of serial positions, indicating that indeed there is virtually no forgetting in the final positions 11 to 13. A regression line is nearly horizontal for positions 11–13 (slope -0.000183) and 1–2 (slope is -0.000112) with the largest decrease over time found in positions 3–8 (slope is -0.0114) and 9–10 (slope is -0.0119). The averaged curve has a slope of -0.00930, with all slopes calculated over the untransformed scores (i.e., not on a logarithmic scale).

(a) Proportion correct, averaged over all serial positions, shown with Dros’ savings scores for comparison. (b) Proportion correct curves for different groups of serial positions and for the average over all 13 positions.

We believe that we may conclude that our attempt to replicate Ebbinghaus’ classic forgetting was successful. We were able to follow his method quite closely and the resulting curve is very similar to both that of Ebbinghaus and that of the two subjects in an earlier German replication [ 21 ], with the exception of the savings value at 31 days, which in our case is much lower than the others. The latter difference remains even if we correct for increased learning time over the course of the experiment. It is possible that with Ebbinghaus and Heller et al. there were far fewer intermediary lists learned between learning and relearning and hence much less interference. Unfortunately, this information is not available, so this must remain speculation.

Effects of serial position on forgetting

Ebbinghaus does not say anything about serial position curves or indeed about the order in which he acquired the syllables. Our data allow us to say a little more about this. When interpreting the serial position scores in Fig 6 , one has to bear in mind the nature of the savings methods with lists of nonsense syllables. With Ebbinghaus-type relearning, a row is always first studied before it is relearned. Savings experiments are very different from normal memory retention experiments where the subject learns something at after some time interval is tested for retention. With savings, the retention measurement itself consists of relearning the original material in repeated recall trials each of which is preceded by prior exposure to the stimulus materials. In theory it is, therefore, possible in extreme cases that there are subjects who can learn a row of 13 syllables in a single learn-recall trial but who subsequently always forget the learned materials in say 10 min. By conventional (non-savings) retention tests, these subjects would be reported as have 0% retention past 10 min, but these same subjects would show 100% savings for all retention intervals past 10 min because when testing their savings performance they would go through the exposure-learn sequence, always (re)learning the materials immediately: at t = 0, they take 1 trial to learn the material and at t = 1 day they also take 1 trial, showing perfect savings (i.e., seemingly no forgetting). Thus, when stimuli are very easy to learn true forgetting becomes impossible to measure with the savings method, because original learning will be at ceiling (one-trial learning) as will be relearning after a time-interval (again: one-trial learning), no matter how long the interval.

Here, it seems that the first two and the last three syllables were very easily learned (and relearned), probably because of primacy and recency effects. This effect was also noticed by other early researchers who adapted Ebbinghaus’ method (e.g., [ 17 ]). Relearning the harder parts of a list, in particular the middle syllables, benefits most from recently having learned these sometime before (i.e., before the relearning phase of the savings experiment), as is evident from the savings scores. The discrepancy between savings and recall or recognition has also been found by other authors (e.g.,[ 24 , 31 , 32 , 33 ]) and appears here as a function of serial position.

The fairly evident primacy and recency effects also suggest that the despite Ebbinghaus’ efforts to construct equivalent stimuli, there is a great variation in how well they eventually were learned: During the first phase (t = 0), stimuli in the first and last positions were very easily and hence very well learned, while those in the middle were learned much less. The effect seems quite constant, however, so that it need not affect the validity of the shape of the forgetting curve, but we should be aware that its shape is based on a combination of very well learned items and just barely learned items. In that sense, the forgetting curve by Ebbinghaus is an average over different forgetting curves of items in various serial positions, which have been learned to varying degrees. This in itself may explain part of the characteristic shape of the curve, which we will explore in the next section.

Curve fitting

Hermann Ebbinghaus [ 8 ] was the first to try to find a mathematical equation that describes the shape of forgetting. Many researchers who used his method have followed suit, also trying to summarize the forgetting curve in a concise equation. In his first manuscript, from 1880, Ebbinghaus proposes the equation x = [1 − (2/ t ) 0.099 ] 0.51 , where x equals 1 minus savings at time t (in min). It is of some interest that Ebbinghaus [ 8 ] (p. 57–63) puts the entire section in which he fits this equation to the data between square brackets, making it an aside: something that is also interesting but not belonging to the main text. Nonetheless, he gives a well-motivated derivation based on a differential equation of a gradually slowing forgetting process. Interesting is that in the write-up of the experiment in 1885 [ 9 ], the equation has been changed to the very different one, which has been become generally known as the ‘Ebbinghaus Forgetting Equation’, rather than the first one, namely Q ( t ) = 1.84 / ((log t ) 1.25 + 1.84), where the log is taken with base 10. This equation is lacking a derivation and Ebbinghaus remarks on it: “Of course this statement and the formula upon which it rests have here no other value than that of a shorthand statement of the above results which have been found but once and under the circumstances described. Whether they possess a more general significance so that, under other circumstances or with other individuals, they might find expression in other constants I cannot at the present time say.” [ 20 ]

We are now in a better position to verify Ebbinghaus’ question about the general significance of his equation by fitting his equations to the other data. The results are given in Table 4 and were obtained with a nonlinear fitting procedure in Mathematica 9 (the Mathematica code is available from the author). We calculated several goodness-of-fit measures including the Akaike Information Criterion or AIC [ 49 , 58 ], which contrary to, for example, variance explained ( R 2 ) or sum of squared differences ( SSD ), takes into account (and penalizes for) the number of free parameters. It also allows a comparison of the goodness-of-fit of different models, even if they have different numbers of parameters. Lower values indicate better fit, where a difference of more than 2 is seen as a meaningful difference in goodness-of-fit [ 49 ].

See text for the meaning of the parameters. SSD is the sum of squared differences between data and fitted curve, R 2 is proportion variance explained, and AIC is the Akaike Information Criterion. To stay close to Ebbinghaus’ own estimates, the parameters are fitted for time expressed in minutes.

In the table, we see that in the case of his power function from 1880 [ 8 ], Ebbinghaus' calculations, carried out by hand, were quite close the computer-optimized parameter values: he found values 0.51 and 0.099 for the parameters, whereas we found 0.523 and 0.101, respectively. For the logarithmic function from 1885 [ 9 ], we also found similar parameters to those parameters Ebbinghaus reported: 1.8 and 1.21 for his values of 1.85 and 1.25 respectively.

The goodness-of-fit of his functions is quite good, in both cases explaining 98.8% of the variance ( R 2 ) for his own data. The equation from 1885 has a slightly smaller SSD value (i.e., fits better), which in fact is the lowest value for an individually fitted curve we obtained (also see below and Table 5 ). Though the equations found by Ebbinghaus fit his own data very well, they do not always fit the other curves well, with especially Mack and Dros showing a relatively bad fit on these ‘classic’ equations. This is perhaps not concluded from the variance explained ( R 2 ), which is very high for all studies, but if we base our judgment on the AIC we observe large differences where the AIC for the Ebbinghaus data is almost twice as low as on the Dros data. This suggests that the general applicability of Ebbinghaus equations may be lacking. We further investigate this by comparing Ebbinghaus’ functions with some other functions that have been proposed in the literature.

See text for the meaning of the parameters. SSD is the sum of squared differences between data and fitted curve, R 2 is proportion variance explained, and AIC is the Akaike Information Criterion. The parameters are fitted for time expressed in seconds.

Ebbinghaus function from 1880 is a type of double power function. A normal power function is described by the equation Q ( t ) = (1+ μ 1 t) − a 1 , where Q ( t ) is savings at time t and μ 1 and a 1 are parameters. The latter equation has been proposed by several authors to describe the time-course of forgetting (e.g., [ 1 , 3 , 4 , 34 ]). The forgetting mechanism typically associated with a power function is a constant slowing down of the forgetting rate with time (cf. Ebbinghaus' account from 1880 mentioned above). Whereas this is certainly a viable mechanism of forgetting, it can be proven mathematically that (spurious) power functions may emerge from averaging over different subjects or items [ 35 , 36 ]. This has also been shown in simulations considering a wide range of circumstances [ 37 , 38 ]. As argued in the previous section, the forgetting curve (also) averages over items that have been learned to various degrees, due to their serial position. There are, therefore, several reasons to expect and consider the power function.

The goodness-of-fit of the simple power function to our data is given in Table 5 . As can be seen, the fit to Ebbinghaus’ data is still impressive, though somewhat less good than either of his own equations. The goodness-of-fit of the power function, as expressed by the SSD , averaged over all four subjects’ is comparable to the Ebbinghaus 1885 ‘logarithmic’ function and it is somewhat worse than his 1880 ‘power’ function. The AIC is slightly worse, but probably not meaningfully so as the difference in AIC measures is only 1.

Heller et al. [ 21 ] also fitted the Ebbinghaus 1885 ‘logarithmic’ equation to the Mack and Seitz data and noticed that it did not fit the Mack and Seitz data well. They therefore proposed a different equation, the sum of two exponentials: Q ( t ) =  μ 1 e − a 1 +  μ 2 e − a 2 A similar function has independently been proposed by Rubin, Hinton and Wenzel [ 39 ] to successfully fit a forgetting curve with very large numbers of observations per data point, which could not be fitted satisfactorily with any of the more than hundred functions studied in Rubin and Wenzel [ 2 ]. None of these authors gave a forgetting mechanism associated with these functions.

Though providing us with a superior fit, a disadvantage of the summed exponential is that there are no memory models that explain why forgetting might have this shape. As remarked by several authors investigating the shape of learning and forgetting [ 35 , 38 , 40 ], simply fitting sums of exponentials is expected to yield progressively better fits for the simple reasons that any function may be approximated by such a sum, which is related to the Laplace transformation.

A model of forgetting and amnesia developed by our group also yields a summed exponential function, but with a different parameterization [ 41 ]. This so called Memory Chain Model assumes that a memory passes through several neural processes or stores, from short-term to very long-term memory. While a memory is (exponentially) declining in intensity in Store 1 (e.g., the hippocampus), its contents is steadily transferred to a Store 2 (e.g., the neocortex) from which it will decline at a lower rate. We still have two exponentially declining stores, as in the summed exponential function above, but they are linked by a memory consolidation process. The decay rates in Store 1 and Store 2 are given by a 1 and a 2 , respectively. The initial strength of the memory traces in Store 1 are given by μ 1 and the rate of consolidating the contents of Store 1 to Store 2 is given by μ 2 . In experiments with dementia patients and experimental animals, Store 1 may typically be identified with the hippocampus and Store 2 with the neocortex. Lesioning Store 1, will produce a retrograde amnesia gradient that can be modeled by the Memory Chain Model simultaneously with the forgetting gradient of healthy controls [ 42 ].

The Memory Chain Model (MCM) equation for type of savings studied here is given by

The MCM function has the same number of parameters but they are arranged differently. The proof that this equation is a mathematical formalization of the memory consolidation process can be found elsewhere [ 42 ]. As can be seen in Table 5 , the summed exponential and the MCM function give exactly the same fits, though the parameters differ. The gain of using the MCM function lies primarily in the fact that its parameters can be interpreted more clearly, that it is associated with a type of consolidation mechanism, and that also explains other types of data than the savings function [ 41 – 43 ]. The MCM function assumes a neural system consolidation mechanism [ 44 , 45 ] that has been dubbed the 'Standard Consolidation Theory' [ 46 , 47 ], where the latter authors propose a different theory, the so called Multiple Trace Theory of consolidation. It is here not our goal to evaluate the merits of these theories; we have reviewed these and other theories of consolidation elsewhere [ 48 ]. We merely want to apply the MCM equation to these four savings curves and evaluate the goodness-of-fit, viewing it as a conceptual improvement of the summed exponential.

If we compare the MCM equation or summed exponential function to the other functions, this only makes sense if we rely on the AIC , which takes into account the varying number of parameters. The MCM function (or double exponential function) fits two of the four curves (Mack and Dros) better than Ebbinghaus’ own equations from 1880 and 1885, it gives about the same fit on the Seitz data, and it does much worse on Ebbinghaus' own data. The average AIC is 0.5 less than the average AIC for the classic Ebbinghaus functions, which—though it indicates a better fit—may not be considered a meaningful difference; a difference of 2 is considered 'meaningful' [ 49 ]. We also fitted a single exponential with only two parameters but this fared far much worse on all data sets, including Ebbinghaus’ data (also see [ 3 ]).

Summarizing, Ebbinghaus' data fit his own equations and the power function best. The AIC indicates that on average the MCM equation (or summed exponential function) is on average better than all equations considered thus far, where the difference with the power function is 1.5. The difference with Ebbinghaus' own equations is only 0.5 but this is partially because his own data have an exceptionally good fit on his own equations, with a very low AIC of about -30.5 (and an extremely high 99.8% variance explained). It is likely, however, that Ebbinghaus actively searched for an equation that achieves such an exceptional fit, which in his eyes was no more than a 'summary' of the forgetting curve (see citation above). This also explains why he has no problems substituting a 'logarithmic' equations for the earlier 'power' equation: it shows a slightly better fit.

Fitting data is always done with a purpose. Ebbinghaus achieved a concise summary of his forgetting data, the power function is a parsimonious description of the forgetting function that shows a good or at least adequate fit in many types of forgetting experiments, and the MCM equation attempts to capture the shape of a hypothetical consolidation process in the brain albeit at the expense of additional parameters. Taking into account these extra parameters, however, does not give a worse fit on the AIC and approaches a meaningful improvement over the power function.

The 24-hour point in Ebbinghaus' forgetting curve

When looking at the shapes of the four curves in Fig 2 , savings after 1 day (or 2 days) seems higher than expected. Ebbinghaus [ 8 ] notices this as well but merely writes it off as a discrepancy from his fitted curve (see above) that still falls within the error bars ([ 8 ], p. 62). He clearly did not trust this data point because in his text from 1885 [ 9 ] he reports that he later had replicated this 24 hour data point. The replicated data for this point gave a very similar score, so we must consider it a valid measurement. Jenkins and Dallenbach [ 50 ], however, interpreted the discrepancy as an effect of sleep, which motivated them to investigate this closer in an experiment on the effect of sleep on forgetting. They also refer to the forgetting curve by Radossawljewitsch [ 16 ], who also found higher savings after both 1 and 2 days (0.689 and 0.609, resp.) compared with after 8 hours (0.474). To them, this is suggestive of a very strong effect of sleep, but Finkenbinder [ 17 ] points out that Radossawljewitsch's 8-hour data point may not be reliable, because these lists were all relearned during the afternoon, when there was less rapid learning resulting in fewer savings. He, therefore, suggests using a corrected savings score at 8 hours of 0.66, which is not unreasonable given that Ebbinghaus also corrected his savings scores for time-of-day effects, in some cases up to 13%. Even if savings would be 0.66 at 8 hours, however, the 1 day savings score is still higher than the 8 hour score and the 2 day savings is still higher than what one would expect.

Using free recall and retention up to 8 hours, the seminal study by Jenkins and Dallenbach [ 50 ] yielded a positive effect of sleep on recall. This effect has since been replicated many times, for example in recent studies on the effects of different sleep stages on both procedural and declarative memory (e.g., [ 51 , 52 – 56 ]). Whereas the older studies from the 1970s and before typically confound the sleep manipulation with time-of-day effects or fatigue, this is no longer the case in the recent studies, so that there is now very strong evidence that sleep does indeed have an effect on memory independent of the effects of, say, rest or lack of interference. In some of the sleep-memory experiments cited above, we even see a temporary increase in the forgetting curve, where subjects score better than after learning in the days following sleep, but not if they skipped the night of sleep after learning (e.g., [ 53 ]). This result—and other studies—suggests that the first night of sleep after learning has a particularly important effect on memory that may continue to evolve for several days afterwards. Such an effect may also be observed in savings curve by Mack and to some extent in the Seitz curve, both show a tendency to increase in savings score for two days following learning.

Given that we can trace the history of research on the effects of sleep on memory to the 24 hour point of Ebbinghaus' forgetting curve, we think it is interesting to evaluate this data point more formally. If we can establish the jump in the curve more formally, it will make a stronger case that the 'true shape' of the long-term forgetting curve has a jump in at 24 hours (or perhaps right after the subject has slept), although we may not conclude from this that the local increase is due to sleep per se, which would require more research is necessary for that.

If we first informally inspect the data points shown in Fig 2 and compare them with the fitted power function, we see relatively less forgetting at either day 1, day 2, or both. In all four panels, at least one of these points is above the fitted power function curve at a distance of at least one standard error. The same is true for the fitted curves of the summed exponential, Ebbinghaus’ 1880 ‘power’ function and his 1885 ‘logarithmic’ function (not shown here). We also see this effect for the Memory Chain Model curve in Fig 3 , though somewhat less pronounced (in the Seitz panel, the fitted curve crosses the error bars at 1 and 2 days). The reason for this is that the Memory Chain Model already incorporates the effects of a hypothetical consolidation process. In short, we observe that there is seems to be a memory ‘boost’ in the classic Ebbinghaus’ forgetting curve and its replications. The current body of research on sleep and memory would predict such a boost after one or two nights and attribute it to sleep, though for this particular type of experiments this has to established more firmly in further experiments. Whatever its cause, we can better quantify the visually observed boost by including it in the equations fitted. We, therefore, made a variant of the power function that differs only in the addition of a constant boost factor to the savings of the retention intervals of 1 day and higher. This power function with boost is also plotted in Fig 2 .

The results are mixed, though on average they suggest a trend towards improvement with a boost parameter. The original power function had a average AIC of -22, a sum-of-squared-differences ( SSD ) of 0.00932 and explained 98.7% of the variance (see Table 5 ), whereas adding the boost reduces the AIC to -23.6, the SSD to 0.00628 and increases the average variance explained to 99.1%, putting its goodness-of-fit on a par with the MCM (or summed exponential) function. A difference in average AIC of 1.6 may perhaps be called a trend towards a meaningful difference, though there were large differences between the individual subjects. The boost parameter in Table 5 shows the size of the upward jump after 24 hours. We see that for Ebbinghaus, this jump is small (0.030), whereas for Mack it is quite substantial (0.131). The Dros data show no evidence for a boost but these fits are probably influenced strongly by the very low 31 day data point.

Concluding Remark

In 1880, Ebbinghaus [ 8 ] set new standards for psychology experiments, already incorporating such ‘modern’ concepts as controlled stimulus materials, counter-balancing of time-of-day effects, guarding against optional stopping, statistical data analysis, and modeling to find a concise mathematical description and further verify his results. The result was a high-quality forgetting curve that has rightfully remained a classic in the field. Replications, including ours, testify to the soundness of his results.

His method can also be seen as a precursor to implicit memory tests in that certain inaccessible representations, seemingly forgotten, can still be relearned faster compared with others that do not show such an advantage. This is evidence of implicit memory because the subjects may not be consciously aware they still possess traces of the memory representations, which cannot be recalled or recognized but that do show savings. The savings method is still used today as a sensitive method to study the decline of foreign languages in order to assess the true extent of linguistic knowledge retained over a long time [ 57 ].

Ebbinghaus [ 8 ] also emphasizes the importance of sleep for memory, but these remarks are limited to how low-quality or insufficient sleep may have inflated his own learning times at certain dates ([ 8 ], p. 66) and as an explanation for the observed time-of-day effects; he learns faster in the morning than at other times. In other words, he acknowledges the effects of previous sleep on current learning, but he does not admit to the role of sleep in slowing down long-term forgetting. The formal analysis above suggests that the classic forgetting curve is not completely smooth but does show a jump at the 1 day retention interval. Current research on the effects of sleep on memory would predict such a jump, but for this particular type of experiment this remains to be established.

Acknowledgments

We would like to thank Annette de Groot and Jeroen Raaijmakers for helpful suggestions when writing this.

Funding Statement

This experiment was conducted as an employee (JM) and student (JD) of the University of Amsterdam. The authors received no specific funding for this work.

Data Availability

Ebbinghaus Forgetting Curve (Definition + Examples)

You might have experienced this before: you cram for an exam, feeling confident about the material, only to forget most of it just a few days later. Why does this happen? Well, let me introduce you to the Ebbinghaus Forgetting Curve, a concept that might shed some light on this phenomenon.

What is the Ebbinghaus Forgetting Curve?

The Ebbinghaus forgetting curve is a graphical representation of the forgetting process. The curve demonstrates the declining rate at which information is lost if no particular effort is made to remember it. The forgetting curve was defined in 1885 by German psychologist Hermann Ebbinghaus (1850-1909) in his book Memory . 

Ebbinghaus was the first psychologist who systematically studied memory and learning. He recorded his findings mathematically in an attempt to discover patterns of forgetting and memory retention. 

Now, imagine a graph where the vertical axis represents how well you remember something, and the horizontal axis represents time. Right after learning, you're at the peak of the graph, but as time goes on, your memory of that information starts to decline, creating a curve that dips downward.

This isn't just about forgetting what you studied for a test... it's a fundamental aspect of how our brains process and store information. The steeper the curve, the faster you forget.

By recognizing the natural decline of memory retention, you can implement strategies like spaced repetition to reinforce what you've learned. This way, instead of feeling frustrated by what you've forgotten, you can harness the power of psychology to make your learning more effective and long-lasting.

In fact, you can test this curve yourself by taking and graphing the results of our memory test .

Ebbinghaus’ experimental method, like that of many of his peers, consisted of conducting a series of extensive tests on himself. He created hundreds of three-letter words, or “nonsense syllables” as he called them, like “wid”, “zof”, and “qax.”

The psychologist then tried to memorize lists made of these words and determined for how long he could remember them after different time intervals. He plotted his results in a graph that we know today as the forgetting curve.  

Rate of forgetting

Ebbinghaus found that the forgetting curve is exponential in nature. It starts off very steep—the amount of retained knowledge drops dramatically soon after we acquire new information. In fact, most of the forgetting occurs within the first hour of learning. And that’s not all. After a day or two, we typically forget around 75% of what we have learned.

Without any additional work, we will quickly forget most of the content of a course, for instance. A week later, it will be as if the learning had never occurred at all. 

Fortunately, there is a point at which the forgetting rate starts to decline at a slower pace. After a day or so, it usually levels off. This is when we can partially absorb essential details and store them in our long-term memory . In other words, the day after taking a course, we will retain only a few details but we will be able to remember them for several more days.

Contributing factors

According to Ebbinghaus, the basic forgetting rate doesn’t differ significantly between individuals. Still, this rate can be influenced to a certain extent by a myriad of different factors.

Prior Knowledge and Meaningfulness of the Subject

If we can connect a lecture to information that is already encoded in our long-term memory, we are more likely to remember it. Our connections to previous knowledge give this information meaning. Research shows that we are more likely to remember information, and remember it for a longer period of time when we give it meaning. 

Similarly, if believe that the information has meaning, whether we have prior knowledge of it or not, it is likely to stick. When our minds believe that something is important, we are more likely to keep our focus on it. If your parent tells you not to forget the phone number they recite to you, you are more likely to hold onto it in your memory instead of listening to the radio or thinking about a casual conversation you had 20 minutes prior. 

The Complexity of the Material 

Before memories are stored as long-term or even short-term memories, they exist in our working memory. Working memory contains information that we are currently “working” with, and if we focus on it for long enough, it will make its way to other types of memory storage. Working memory is limited to only a few items. 

For this reason, complex material may be hard to remember. Our working memory can only focus on so much before it discards the information. If we have nothing to connect complex material to or cannot focus on it for a long period of time, it will get lost. 

This doesn’t mean we can’t learn complex material. We just have to find ways to break down the material into pieces of information that we can work with and remember. Building a strong foundation in a subject will make it easier to take in complex concepts. 

How the Information Is Presented

The person presenting the information also impacts whether the information will “stick.” Simplicity is key to presenting information, but other factors also influence how complex, meaningful, or memorable it is. 

If you want to present meaningful information, follow these guidelines.

• Repeat the information over and over. Repetition makes information stick.
• Tell the information as if it’s a story. People follow the format of stories. They know stories have a beginning, middle, end, and problem that is ultimately solved. If you can position the information in this way, your audience is more likely to follow and attach meaning. 
• Be clear and confident. When you are clear about the information, there is little room for the audience to question your information or to search for a deeper meaning in it.  

Individual Capability

Some people naturally have a better memory than others! Genetics isn’t the only factor that influences memory, but it does play a role. If you are someone that doesn’t naturally have a great memory, you can still take steps to take care of your brain and learn your material in effective ways. Taking care of your body and mind will take care of your memory.

Physiological Factors (Lack of Sleep, Hunger, etc.) 

Did you know that sleep “resets” the brain ? Sleep is crucial for memory storage and brain health. Getting eight hours of sleep a night can improve your ability to restore memories from the day before. If you’re getting very few hours of sleep every night, you may be overworking your brain and preventing it from storing memories properly! 

Other physiological factors may also play a role in memory. Recently, scientists discovered that the “hunger hormone” plays a big role in episodic memory . But this doesn’t mean that you should starve yourself before a lecture just to remember the material. Scientists are still looking into how the hunger hormone, the vagus nerve, and long-term memory work together (or against each other!) 

Psychological Factors (Stress, Anxiety, etc.)

Stress and anxiety can negatively affect your ability to form and store memories. If you are under great stress during an event, you may not be able to remember it. Details may be fuzzy, or you may not pick up on everything that was happening around you leading up to, or after, an event.

Knowing this connection between stress and memory is crucial for people who may want to retrace their steps or restore memories of an event. When we recall memories, we want to have all the details. When we don’t, our brains fill in the details for us. These details may not always be accurate. People can create false memories when they are under stress or trying to recall stressful memories from their lives.

Overcoming the Forgetting Curve

While some aspects that contribute to the speed of forgetting cannot be changed, Ebbinghaus proposed the use of two methods when we purposefully acquire new skills or knowledge: mnemonic techniques and repetitions. Implementing these strategies can help us overcome the forgetting curve. 

Mnemonic techniques 

Mnemonic learning techniques rely on “repackaging” of the information, a process that helps the brain to store the information and find it again when needed. This strategy is based on creating associations with something that is easy to remember. For example, mnemonics use images, emotions, patterns, or rhymes like the alphabet song, to help us absorb the information more efficiently.  

You probably have used these techniques before in school. "Every Good Boy Does Fine" helps music students figure out how to read music. "PEMDAS," or "Please Excuse My Dear Aunt Sally," spells out the order of operations in math. We remember them, years after we graduated high school, because they're so memorable!

Repetitions of information 

Ebbinghaus showed that repeating and reviewing the acquired knowledge helps strengthen our memory. It is clear from the forgetting curve pattern that the initial repetition of the information should ideally occur within the first day of learning. 

But this is not enough. While an initial review of what we’ve learned certainly helps us remember the details in the short term, reviewing multiple times will enable us to retain them for much longer. Each time we revisit the same material, we retain larger chunks of information. As a result, the forgetting curve will start flattening out at a much higher level. That means we will forget at a slower pace. 

In order to retain knowledge and fully embed the learned material into our long-term memory, we have to periodically review the information. Research indicates that a minimum of three reviews is necessary for obtaining the best results.

Ebbinghaus argued that each subsequent repetition increases the time needed before the next one. This is called spaced learning. 

How to Use the Ebbinghaus Forgetting Curve with Spaced Learning

For Ebbinghaus, overcoming the forgetting curve had more to it than just simple repetitions. 

Repetitions have to be spaced for optimal effect. Repeating new facts many times within an hour is not useful in overcoming the forgetting curve. If we are not required to make any attempt to recall and retrieve the information, the improvement is impossible because the memory hasn’t had a chance to decay. At the same time, if the information is repeated too infrequently, retention and recall will fail. In this case, we will have to start the learning process all over again.

However, when the material is being repeated at strategically spaced intervals, the brain reconstructs the memory and strengthens it in the process. These specific time intervals between multiple learning sessions are essential. They allow the brain to recover between repetitions and consolidate the learning. 

We can recall information and concepts better if we learn them in the course of several spread-out sessions.

Spaced learning is much more effective than massed learning where we try to cram all the information into a short period of time. In fact, the massed learning technique turns out to be hugely counterproductive. Spaced learning, on the contrary, enables us to better manage the information that is retained and increase our long-term productivity. It leads to a better overall learning experience and ultimately allows us to reshape the forgetting curve.     

It is not necessary to review the information in exactly the same way in which the initial learning occurred. Presenting the same concept in a slightly different form like a video or exercise is just as efficient in strengthening the memory and overcoming the Ebbinghaus forgetting curve. 

Related posts:

• 5 Theories of Forgetting (Memory)
• Motivated Forgetting (Examples!)
• Serial Position Effect (Example + Definition)
• The Spacing Effect in Learning and Retention
• Free Memory Test (5 Mins + Instant Results)

Reference this article:

About The Author

Memory Topics:

Free Memory Test

Serial Position Effect

Primacy Effect

Recency Effect

Short Term Memory

Sensory Memory

Working Memory

Long Term Memory

Episodic Memory

Semantic Memory

False Memories

Photographic Memory

Memory Tricks

Memory Palace

Rote Memorization

Atkinson and Shiffrin Model

Proactive Interference

Retroactive Interference

State Dependent Memory

PracticalPie.com is a participant in the Amazon Associates Program. As an Amazon Associate we earn from qualifying purchases.

Follow Us On:

Youtube Facebook Instagram X/Twitter

Psychology Resources

Developmental

Personality

Relationships

Psychologists

Serial Killers

Psychology Tests

Personality Quiz

Memory Test

Depression test

Type A/B Personality Test

© PracticalPsychology. All rights reserved

Privacy Policy | Terms of Use

Hermann Ebbinghaus and the Experimental Study of Memory

Hermann Ebbinghaus (1850 – 1909)

On January 24, 1850, German psychologist Hermann Ebbinghaus was born. Ebbinghaus pioneered the experimental study of memory, and is known for his discovery of the forgetting curve and the spacing effect .

“When we read how one mediæval saint stood erect in his cell for a week without sleep or food, merely chewing a plantain-leaf out of humility, so as not to be too perfect; how another remained all night up to his neck in a pond that was freezing over; and how others still performed for the glory of God feats no less tasking to their energies, we are inclined to think, that, with the gods of yore, the men, too, have departed, and that the earth is handed over to a race whose will has become as feeble as its faith.” – Hermann Ebbinghaus (1885) [8]

Hermann Ebbinghaus – Early Years

Hermann Ebbinghaus was born in Barmen , in the Rhine Province of the Kingdom of Prussia and attended the University of Bonn where he intended to study history and philology. In 1870, his studies were interrupted when he served with the Prussian Army in the Franco-Prussian War. Ebbinghaus evolved a great interest in philosophy and finished his dissertation on Eduard von Hartmann ‘s Philosophie des Unbewussten ( Philosophy of the Unconscious ). After earning his doctorate degree in 1873, Ebbinghaus spent much time in Halle and Berlin and also traveled through England and France. It is assumed that Ebbinghaus took teachers positions while on travel and apparently he discovered Gustav Fechner ‘s book Elemente der Psychophysik ( Elements of Psychophysics ) while in London.[ 4 ] The book highly inspired the young scientist to start his own research on memory studies.

Experimental Psychology

Ebbinghaus’ famous work, Memory: A Contribution to Experimental Psychology was already published in 1885 and was so successful that he was appointed professor at the University of Berlin. Ebbinghaus and Arthur König founded the Psychological journal Zeitschrift für Physiologie und Psychologie der Sinnesorgane in 1890. Ebbinghaus joined the University of Breslau, Poland and studied how children’s mental ability declined during the school day. He also founded a psychological testing laboratory there. Die Grundzüge der Psychologie where published in 1902, which was an instant success. Two years later, Ebbinghaus moved to Halle. His last and quite successful work Abriss der Psychologie ( Outline of Psychology ) was published in 1908.

Prior Knowledge, Understanding, and Learning

Contrary to most scientists studying higher mental processes, Ebbinghaus believed that research could be conducted through experiments. He developed a system recognizing the fact that learning is always affected by prior knowledge and understanding. Ebbinghaus figured that he would need something that would be memorized easily but without prior cognitive associations. The scientist created the so called “ nonsense syllables “. This can be understood as a consonant-vowel-consonant combination, where the consonant does not repeat and the syllable does not have prior meaning, like DAX, BOK, and YAT. After creating the collection of syllables, Ebbinghaus pulled out a number of random syllables from a box and then write them down in a notebook. Then, to the regular sound of a metronome, and with the same voice inflection, he would read out the syllables, and attempt to recall them at the end of the procedure. One investigation alone required 15,000 recitations.

The Forgetting Curve

However, there were also some limitations in Ebbinghaus’ work on memory. For instance, he was the only subject in the study and therefore it was not generalizability to the population. Also, a large bias is to be expected when a subject is a participant in the experiment as well as the researcher. Still, Ebbinghaus managed to contribute significantly to the research on memory. His most famous finding is probably the forgetting curve , which describes the exponential loss of information that one has learned. His results roughly state that just 20 minutes after learning, we can only recall 60% of what we have learned. After one hour, only 45% of what has been learned is still in our memory, and after one day only 34%. Six days after learning, the memory has already shrunk to 23%; only 15% of what has been learned is permanently stored.

The Ebbinghaus Illusion – The two orange circles in the middle are the same size.

The Ebbinghaus Illusion

In the most famous version of this illusion, two circles of identical size are placed close to each other and one is surrounded by large circles while the other is surrounded by smaller circles; the first central circle appears smaller than the second central circle. This illusion has been used extensively in research in cognitive psychology to learn more about the different perceptual pathways in our brain. In the English-speaking world, the circles were published by Edward Bradford Titchener in a book on experimental psychology in 1901, hence their alternative name Titchener circles .

Shortly after the publication of  Abriss der Psychologie , on February 26, 1909, Ebbinghaus died from pneumonia at the age of 59.

References and Further Reading:

• [1]  Hermann Ebbinghaus at the Human Intelligence
• [2]  Hermann Ebbinghaus at Famous Psychologists
• [3]  Hermann Ebbinghaus at Britannica
• [4]  Gustav Fechner and Psychophysics , SciHi Blog, April 19, 2016.
• [5]  Works by or about Hermann Ebbinghaus  at  Internet Archive
• [6]  Hermann Ebbinghaus at the Human Intelligence website
• [7]  Ebbinghaus, H. (1885).  Memory: A contribution to experimental psychology .  New York: Dover.
• [8]  Ebbinghaus, H.  “ Experiments in Memory ,” in   Science   Vol. 6, 1885, p. 198
• [9] Hermann Ebbinghaus at Wikidata
• [10]  Chris Dula,  Memory: Forgetting Curve and Serial Position Effect , 2014, East Tennessee State University @ youtube
• [11] Ebbinghaus, H. (1908).   Psychology: An elementary textbook.   New York: Arno Press.
• [12] Timeline of German Psychologists , via DBpedia and Wikidata

Tabea Tietz

Related posts, emmy noether and the love for mathematics, carsten niebuhr and the decipherment of cuneiform, karl friedrich schinkel and the prussian city scapes, august leopold crelle and his journal, one comment.

Pingback: Wheel’s Gazette: Year 2, Vol. #29 | Whewell's Ghost

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Further Projects

• February (28)
• January (30)
• December (30)
• November (29)
• October (31)
• September (30)
• August (30)
• January (31)
• December (31)
• November (30)
• August (31)
• February (29)
• February (19)
• January (18)
• October (29)
• September (29)
• February (5)
• January (5)
• December (14)
• November (9)
• October (13)
• September (6)
• August (13)
• December (3)
• November (5)
• October (1)
• September (3)
• November (2)
• September (2)

Legal Notice

• Privacy Statement

Thinkific Plus

Ebbinghaus forgetting curve, share this article.

If you’ve ever watched a video on a new topic, only to wake up the next morning and not remember any of the details you just learnt, you’ve fallen victim to the forgetting curve.

The Ebbinghaus forgetting curve is a psychological model linked to memory and how we forget things over time. Here’s what you need to know about the forgetting curve, plus strategies to combat the forgetting curve – for both learners and educators.

Click the links below to skip ahead:

What is the forgetting curve in psychology?

• Understanding the Ebbinghaus Forgetting Curve

How does the forgetting curve work?

Forgetting curve example, the science behind the forgetting curve, used spaced repetition, practice active learning, break content into chunks, test memory with retrieval games, take advantage of visual aids, try a multisensory approach to learning, mix it up with interleaving, schedule spaced practice, incorporate teach and explain activities, apply the knowledge in real life, the ebbinghaus forgetting curve in remote learning.

The Forgetting Curve – aka the Ebbinghaus Forgetting Curve – is a graphical model that shows how we forget information over time. The curve maps how our memory declines –  as more time passes, the percentage of information we retain reduces.

Understanding the Hermann Ebbinghaus forgetting curve

The Ebbinghaus Forgetting Curve takes its name from Hermann Ebbinghaus, a German psychologist who systematically studied memory and learning in the 19th century. Here’s what you need to know.

Who was Hermann Ebbinghaus and why was he important?

It’s hard to overstate the influence of Hermann Ebbinghaus on experimental psychology. Building on the work of a German philosopher named Herbart, Ebbinghaus was one of the first researchers to tackle experiments on the shape of forgetting and he conducted a series of thorough and detailed experiments, which he eventually completed and published between 1880-1885.

Ebbinghaus’ forgetting curve is based on 7 months of experimenting on himself – sometimes for up to 3 sessions each day. Ebbinghaus carried out his original research on himself, first using numbers, tones, and poem stanzas to test his own memory and patterns of forgetting. 

But he quickly realized that these materials weren’t right for his experiments as they contained too much variability. Instead, Ebbinghaus settled on using nonsense syllables – like “Zof”, “Qax”, and “Wid” – these had more uniform characteristics than existing poems, words, and other verbal material. 

The introduction of nonsense syllables was one of the first to use controlled, artificial stimuli in psychology and a wide range of experimental psychology experiments borrowed from his methods.

Later Ebbinghaus tested his results using verses from Don Juan by Byron which confirmed his original results, but also resulted in a wider variance in the data compared to the nonsense syllables. Numerous experimental psychologists have replicated Ebbinhaus’ forgetting experiments – and generated similar results.

The forgetting curve maps the decline of memory retention over time – and the curve shows how information is lost over time. But there are 6 more aspects of memory and forgetting that also play a role:

The changing rate of forgetting

The forgetting curve is exponential – it drops steeply as the amount of retained knowledge drops as soon as we acquire new information. 

In fact, most forgetting happens within the first hour of learning. We typically only remember around 75% of information at the end of a lecture. And within a couple of days, you’ve forgotten as much as 75% of what you learnt.

The rate of forgetting levels off after this point, so memory declines at a slower pace. That means you’ll only be able to remember a few key details from the lecture after a couple of days – but you’ll usually be able to remember those things for several days.

The importance of meaningfulness and prior knowledge

When it comes to retaining information, prior knowledge has a big role to play in how likely we are to remember something. If you can link new learning content to something you’ve learnt before, it’s easier to remember.

Meaningfulness also impacts memory – when information has meaning, you’re more likely to remember it. For example, you can remember directions for how to get to your friend’s house easier than remembering the content of the video you watched the same day because of the importance of that information. The directions have more meaning, so your memory is more likely to hold onto it.

The same goes for topics that you’re interested in or that are relevant to you – looking at case studies or listening to people you identify with, for example, can be more effective than reading a regular text book.

Things that don’t have meaning – like nonsense syllables – fit the forgetting curve closest, whereas topics that both have meaning and where you have prior knowledge are more likely to stick.

How information is presented

The way something is communicated can also impact how easy it is to remember. Simplicity is critical here. It’s easier to remember information that’s presented in a simple, straightforward way with plenty of visual aids like diagrams and infographics – compared to a block of featureless text.

The influence of complexity

The rate of forgetting is influenced by the complexity of the material you’re trying to learn too. Complex material is generally harder to remember. That’s because our working memory can only focus on a small number of things at a time – and when it hits the limit, it discards information and that complex material is lost.

Individual variations in memory

The forgetting curve is generally the same for everyone and the basic rate of forgetting doesn’t vary much between individuals – but some people do have better memory than others. That means you can expect to see differences within a class of learners between who remembers what information and how much they remember.

As well as variations in prior knowledge and the meaningfulness of the content – which we know impacts the forgetting curve – there are also other factors that come into play.

Some factors that influence memory include:

• Environment
• Concentration

While the forgetting curve is a general model that applies to all learners, there are small individual variations that impact how much you can remember compared to your peers.

Physiological and psychological factors

Physiological factors also have a critical role to play in memory retention – for example, lack of sleep significantly impacts your ability to learn and retain information. Hunger and nutrition can also impact memory processing.

Then there’s the influence of psychological factors. Stress and anxiety can negatively affect your ability to store and retain information. This is important for learners as stress and anxiety can often lead to a vicious cycle – learners feel stressed, which makes it harder for them to retain information, which leads to more stress, and the cycle continues.

The Ebbinghaus Forgetting Curve looks like this – with the percentage of information retained on one axis and the elapsed time on the other axis.

You can see the rate of change on the graph with most information being lost within the first hour before leveling off as more time passes.

The human brain has an incredible ability to acquire, store, and recall information we learn through our lifetimes. Memory is an active process – we have to use effort to rebuild memories and recall concepts, ideas, and information that we’ve learnt across decades.

While forgetting has long been seen as a negative aspect of memory, scientists now believe that forgetting may be a functional feature of the brain – as it allows our brains to interact dynamically with the environment. While forgetting can be annoying, forgetting some memories can be beneficial as it leads to more flexible behavior and better decision-making.

Research now suggests that forgetting is due to changing memory access, rather than memory loss and decay. The information is still stored in the brain, but you can’t access it anymore.

The good news? There are strategies you can use to help you remember what you’ve learnt – and make the learning process more fun.

10 methods to fight the forgetting curve

While you can’t avoid the forgetting curve completely, here are 10 strategies you can use to improve knowledge retention and reduce the impact of the forgetting curve.

The forgetting curve shows how information is forgotten over time if it isn’t reviewed or revisited. But Ebbinghaus also found that information is easier to recall when it’s reinforced – that means you need to revise the content after you’ve learnt it.

One of the top methods to improve memory is to review learning material at spaced intervals. Instead of trying to cram all the information in at once, repeat the learning multiple times after the initial lesson. This strategy helps to increase memory retention and keep the information fresh in your mind.

Check out the graph below to see how it works.

For educators: To use this method in practice, make sure that you provide learners with follow-up activities to help them review the information they covered in the original lesson. Try using short refresher videos, quizzes, and flashcards.

For learners: Set yourself a schedule to review the information you’ve learnt – including immediately after the learning period, and then for the next 3 days afterwards. Try setting reminders on your phone to help you remember when it’s time to do a repetition exercise.

You can boost your knowledge retention by practicing active learning – that means actively engaging with the material you’re learning rather than passively watching or listening by taking notes, summarizing key points, sketching pictures and more.

Actively processing information as you learn it helps to reinforce the memory process. To help this process, educators can use fun activities like asking learners to share their real-time reactions to videos or lesson content, either using a messenger app, chat function, or live tweeting using a class-specific hashtag. This helps learners engage with the content, identify trends, and evaluate other learners’ points of view as they go.

For educators: To encourage active learning, set active tasks for learners – like taking 5 minutes to explain the material they’ve just learnt to a partner or taking a quick quiz.

For learners: Make sure you’re engaging with learning material by taking notes, summarizing what you’ve covered, and writing or making voice notes about the topic in your own words.

If you want to learn and retain knowledge of complex topics, break it down into manageable chunks. The chunking method helps to improve memory – while also making learning content more fun!

The aim with content chunking is to divide learning content into smaller, bite-sized bits of information that can be easily understood, learnt, and remembered. Chunking also helps to make the learning process more engaging and dynamic.

For educators: Chunking content starts with you – take lessons and modules and trim them down into shorter, quicker modules, no longer than 20 minutes long to make sure you hold your learners’ attention. Check out microlearning techniques for more ideas!

For learners: If you’re faced with a big topic to learn, practice chunking by breaking the content down into smaller bits – write your notes in chunks too, picking out the most essential information as bullets, lists, and single sentences to make it easier to remember.

To help improve your recall, use retrieval activities to encourage your brain to access information from your memory – rather than simply re-reading or re-watching the learning content. 

Testing yourself on the material you’ve learnt from memory helps to reinforce your knowledge, while also giving you the satisfaction of seeing how much you can remember! Make it fun with quizzes or games rather than regular tests.

For educators: To help learners with memory retrieval, set regular closed-book practice tests and quizzes that cover the learning materials for every topic. Use a tool like Kahoot! to make collaborative quizzes online.

For learners: Try using the copy-cover-and-check method to test yourself as you’re learning – cover up your notes and attempt to recall the information, then check how you did. You can also use flashcards as a memory retrieval tool too.

One of the most effective methods to help you store information in your long-term memory is to pair concepts with images and visuals. Visuals can help you make sense of content – if you can hook an idea to an image, you can increase the chance that it will be stored into your long-term memory and that you’ll remember it down the line.

Visual aids can include:

• Infographics

For educators: Use visuals throughout your learning content to help concepts and ideas stick in your learners’ minds – encourage learners to try ‘sketchnoting’ where they sketch a quick picture of what they’ve learnt rather than taking traditional written notes to help learners visualize their understanding.

For learners: Look for graphics and diagrams to help reinforce what you’ve learnt, or make your own! Make sure they’re as clear and as simple as possible for best results. If you’re a visual learner , try to find video-based learning content to help you absorb information.

If you want something to stick in your mind, take a novel approach to learning by making the process multisensory. Those 5 senses activities aren’t just for kindergarteners – learners of all ages can benefit from activities that engage the senses, like touch, smell, and taste. 

Some ideas include:

• Hands-on science experiments
• Field trips and in-person activities
• Role playing
• Dances and hand movements
• Themed board games

For educators: Develop learning content with multisensory activities in mind – even if your learners are remote, you can encourage multisensory learning by setting tasks and activities that encourage them to get up from their desk and engage with the learning content. Try matching hand movements and dances to key concepts to make them easier to remember.

For learners: Try using multisensory stimuli like taste, smell and sounds to help you learn information. In fact, certain smells may help memory and concentration – including lavender and rosemary essential oils. 

Interleaving is an effective strategy to enhance long-term memory retention, as well as improving problem-solving skills. The Interleaving technique involves mixing – or interleaving – two or more topics while learning.

Rather than focussing on just one concept, educators can use interleaving to improve memory by switching back and forth between two or more concepts or topics. Teach one concept, spend time explaining it, then move onto another concept before revisiting the original concept later.

Interleaving promotes comparison and contrast, which can increase learners’ understanding of a topic and their knowledge retention.

For educators: Use interleaving to add context to certain topics or concepts e.g. interleaving the histories of different countries based on a common theme. Avoid interleaving topics that are too similar or too different so you don’t risk confusing learners.

For learners: When it comes to self-study, use interleaving to spread topics and concepts through your study sessions, rather than learning everything about one topic in one block. This can help you remember and retain the information for longer.

When it comes to memory, when you learn can be just as important as what you learn. 

Instead of trying to cram learning content, the most effective method for your memory is to space out learning over multiple sessions – known as spaced practice. Schedule spaced learning activities over time, such as 1 hour every day leading up to a test, rather than a single 8-hour session.

If you can spread your learning across multiple days or weeks, you can improve memory retention and reduce the likelihood of you forgetting the content. It also means there’s less pressure on you to learn everything you need to know by cramming. Every session gives you the chance to go back over what you’ve learnt in previous sessions, review the material, and enhance your understanding of the topic or concept.

For educators: Add activities into your learning content that encourage learners to revisit previous topics – such as creating a worksheet with picture prompts to help learners remember and explain key concepts from memory.

For learners: To take advantage of this method, plan your spaced learning schedule in advance so you know what you need to be reviewing every day. This can help you feel more in control and reduce stress – especially before a test or exam.

Boost knowledge retention with activities that encourage learners to try teaching the material to others or imagine explaining it to someone else. Teaching helps to solidify learners’ own understanding of the content, while also strengthening memory recall.

For example, the Jigsaw method divides learners into groups – and each person in the group is given a different topic to explore. After having time for knowledge refresh and further research, learners regroup with learners from other groups who were exploring the same idea to confirm their understanding. Then the original groups come back together and each learner teaches the topic to their original group.

For educators: Use learning communities to encourage more social learning activities to boost knowledge retention even in remote learning contexts, including ‘teach and explain’ activities like presentations and explanations.

For learners: Try to find a study buddy to do different teach and explain activities together – you can do them remotely via live video calls or record yourself explaining concepts and send them to your study partner or online learning community.

Use real-world examples to improve knowledge retention by finding practical scenarios and real-life problems and debates that related to the learning content. If you can embed learning in specific, meaningful contexts, you’re more likely to form stronger memory connections that are easier to recall down the line.

For educators: Include activities that encourage learners to apply their knowledge to real-life scenarios and examples, such as debating a viral topic in your industry or field. Either pair students one-on-one or start a whole-class discussion.

For learners: Research real-life case studies of the concepts you’ve learnt to see how they’re applied in practice – when it comes to recalling the information, you’re more likely to remember it if you’ve linked the knowledge to a concrete place, problem, or group of people.

For Creator Educators, understanding the forgetting curve is essential to help you create and rollout content that boosts knowledge retention. As your learning content is unlikely to be delivered in-person, you need to find strategies to help you reinforce learning through remote activities and assignments that activates your learners’ knowledge and sparks their imaginations.

Here are some ideas to get you started:

• Use microlearning concepts: Microlearning is now a top technique for Creator Educators thanks to its bite-sized approach to learning that’s ideal for engaging learners and chunking complex topics to limit the forgetting curve. Try using microlearning techniques like short videos, quizzes, and audio clips to engage learners.
• Utilize social media: While it can be hard to compete against the huge quantity of learning content on social media, if you can encourage learners to use social media to reinforce their learning over time then you can work to reduce the forgetting curve – think themed hashtags, discussion threads, and short videos to help them revise key concepts.
• Generate group discussions: If you have a dedicated online community space, use group discussions to encourage learners to recall and revisit critical learning topics and concepts, while also boosting social learning opportunities by encouraging more interaction and engagement within your group.
• Try digital flashcards: Digital flashcards help learners to reinforce their learning on-the-go, without the need for a partner or physical classroom setting. Create flashcards for your content or encourage learners to make their own.
• Use case studies: Case studies are a really effective method to help improve understanding and knowledge retention in learners, wherever they’re working from. Encourage learners to find case studies that are relevant or interesting to them to help make the content more meaningful, personal, and interactive.

Don’t get caught out by the forgetting curve

Both educators and learners can benefit from understanding the forgetting curve – and taking active steps to reduce the effects of forgetting. Though every learner will experience Ebbinghaus’ forgetting curve in some way or another, with the right learning strategies you can improve knowledge retention and long-term memory for better results.

Colin is a Content Marketer at Thinkific, writing about everything from online entrepreneurship & course creation to digital marketing strategy.

• KWL Chart Teaching & Learning Strategy: Examples, Templates, and Other Strategies
• 10 Instructional Design Models For Creating A Winning Online Course
• 7 Instructional Design Storyboard Templates For Online Course Creators
• 10 Steps To Creating A Wildly Successful Online Course
• What Is Customer Lifetime Value (CLV) and Why Does It Matter?

Related Articles

The roi of compliance training: attribution & business case (calculator �....

Explore the concept of Compliance Training ROI (Return on Investment). Understand the hidden benefits and cost savings.

Customer Engagement: A Comprehensive Guide

Customer engagement is essential for any business that wants to stand out from the competition. Learn what it takes to build a successful customer engagement strategy.

Training Audit Checklist: A Comprehensive Guide for Effective Evaluation

Training audit guide and checklist designed to help you identify areas for improvement and ensure compliance.

Try Thinkific for yourself!

Accomplish your course creation and student success goals faster with thinkific..

Download this guide and start building your online program!

It is on its way to your inbox

• Living reference work entry
• First Online: 20 January 2021
• Cite this living reference work entry

• Gustavo Munro 3 ,
• Mario A. Laborda 3 ,
• Gonzalo Miguez 3 &
• Vanetza E. Quezada-Scholz 3  

107 Accesses

Hermann Ebbinghaus (1850–1909) is considered one of the experimental psychologist’s pioneers. He was one of the first to investigate memory using an experimental paradigm, heavily contrasting with the predominant unscientific approaches used by psychologists of his era. Despite the lack of proper control procedures in his work – relative to what we know today – he demonstrated that memory is a phenomenon that can be measured and studied scientifically.

About His Life

Ebbinghaus was born on the 24th of January of 1850 in the former Kingdom of Prussia, specifically in the city of Barmen which belongs to the province of Rhine. Son of a prestigious and wealthy merchant (Carl Ebbinghaus), he had a traditional evangelical education, and as soon as he was able, he joined the University of Bonn and later the University of Berlin and Halle (Shakow 1930 ). He studied history and philology at first, but quickly gained an interest in philosophy. Nonetheless, his studies were interrupted by the...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Ebbinghaus, H. (1902). Grundzüge der Psychologie . Leipzig: Veit & Comp. Retrieved from https://archive.org/details/grundzgederpsy01ebbi/page/n5/mode/2up .

Book   Google Scholar  

Ebbinghaus, H. (1908). Psychology: An elementary text-book (trans: Meyer, M.). Boston: D.C. Heath & Co. Publishers. https://doi.org/10.1037/13638-000 .

Ebbinghaus, H. (1913). Memory: A contribution to experimental psychology, 1885 (trans: Ruger, H. A., & Bussenius, C. E.). New York: Columbia University. Teachers College. Educational reprints. https://doi.org/10.1037/10011-000 .

Fechner, G. T. (1948). Elements of psychophysics, 1860. In W. Dennis (Ed.), Century psychology series. Readings in the history of psychology (pp. 206–213). New York: Appleton-Century-Crofts. https://doi.org/10.1037/11304-026 .

Chapter   Google Scholar  

Herb, K. (1989). Das Julius-Ebbinghaus-Archiv. Forschungsbericht über ein Projekt der Deutschen Forschungsgemeinschaft. Kant-Studien, 80 , 345–353.

Google Scholar  

Holling, H., & Erdfelder, E. (2007). The new Zeitschrift für Psychologie/Journal of Psychology. Zeitschrift für Psychologie/Journal of Psychology, 215 (1), 1–3. https://doi.org/10.1027/0044-3409.215.1.1 .

Article   Google Scholar  

Kjerstad, C. L. (1919). The form of the learning curves for memory. Psychological Monographs, 26 (5), i–89. https://doi.org/10.1037/h0093186 .

Murdock, B. B. (1985). The contributions of Hermann Ebbinghaus. Journal of Experimental Psychology: Learning, Memory, and Cognition, 11 (3), 469–471. https://doi.org/10.1037/0278-7393.11.3.469 .

Newell, A., & Rosenbloom, P. S. (1981). Mechanisms of skill acquisition and the law of practice. In J. R. Anderson (Ed.), Cognitive skills and their acquisition (pp. 1–51). Hillsdale: Erlbaum.

Postman, L. (1968). Hermann Ebbinghaus. American Psychologist, 23 (3), 149–157. https://doi.org/10.1037/h0025659 .

Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and non-reinforcement. In A. H. Black & W. F. Proktsy (Eds.), Classical conditioning II: Current research and theory (pp. 64–99). New York: Appleton-Century-Crofts.

Roediger, H. L. (1985). Remembering Ebbinghaus. Contemporary Psychology, 30 (7), 519–523. https://doi.org/10.1037/023895 .

Sammut, C., & Webb, G. I. (Eds.). (2010). Encyclopedia of machine learning . Boston: Springer. https://doi.org/10.1007/978-0-387-30164-8 .

Shakow, D. (1930). Hermann Ebbinghaus. The American Journal of Psychology, 42 (4), 505–518. https://doi.org/10.2307/1414874 .

Witnauer, J. E., Urcelay, G. P., & Miller, R. R. (2014). The error in total error reduction. Neurobiology of Learning and Memory, 108 , 119–135. https://doi.org/10.1016/j.nlm.2013.07.018 .

Article   PubMed   Google Scholar  

Wright, T. P. (1936). Factors affecting the cost of airplanes. Journal of the Aeronautical Sciences, 3 (4), 122–128. https://doi.org/10.2514/8.155 .

Download references

Author information

Authors and affiliations.

Department of Psychology, University of Chile, Santiago, Chile

Gustavo Munro, Mario A. Laborda, Gonzalo Miguez & Vanetza E. Quezada-Scholz

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Vanetza E. Quezada-Scholz .

Editor information

Editors and affiliations.

Oakland University, Rochester, MI, USA

Jennifer Vonk

Department of Psychology, Oakland University Department of Psychology, Rochester, MI, USA

Todd Shackelford

Section Editor information

Ruhr University, Bochum, Germany

Alexis Garland

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this entry

Cite this entry.

Munro, G., Laborda, M.A., Miguez, G., Quezada-Scholz, V.E. (2021). Ebbinghaus. In: Vonk, J., Shackelford, T. (eds) Encyclopedia of Animal Cognition and Behavior. Springer, Cham. https://doi.org/10.1007/978-3-319-47829-6_89-1

Download citation

DOI : https://doi.org/10.1007/978-3-319-47829-6_89-1

Received : 23 October 2020

Accepted : 17 November 2020

Published : 20 January 2021

Publisher Name : Springer, Cham

Print ISBN : 978-3-319-47829-6

Online ISBN : 978-3-319-47829-6

eBook Packages : Springer Reference Behavioral Science and Psychology Reference Module Humanities and Social Sciences Reference Module Business, Economics and Social Sciences

• Publish with us

Policies and ethics

• Find a journal
• Track your research

Ebbinghaus Forgetting Curve

Ebbinghaus forgetting curve describes the decrease in ability of the brain to retain memory over time. The issue was hypothesized by Hermann Ebbinghaus in 1885, which is why it’s called Ebbinghaus forgetting curve.

The theory is that humans start losing the memory of learned knowledge over time, in a matter of days or weeks, unless the learned knowledge is consciously reviewed time and again. A related concept to the forgetting curve is strength of memory , which states that the time period up to which a person can recall any memory is based on the strength of the particular memory.

The first study to hypothesize the forgetting curve was done in 1885. Mathematically, the formula that can describe the phenomenon is

R refers to memory retention, S refers to relative strength of memory and t refers to time.

Hermann published is first study about the forgetting curve in German, which was later translated to be called Memory: A contribution to Experimental Psychology.

Ebbinghaus conducted a series of tests on himself, which included memorization and forgetting of meaningless three letter words. Ebbinghaus memorized different nonsense words such as “WID”, “ZOF and “KAF”, and then he tested himself to see if he could retain the information after different time periods. The results thus obtained were plotted in a graph, which is now referred to as the forgetting curve.

Ebbinghaus found the forgetting curve to be exponential in nature. Memory retention is 100% at the time of learning any particular piece of information. However, it drops rapidly to 40% within the first dew days. After which, the declination of memory retention slows down again.

In simple words, forgetting curve is exponential because memory loss is rapid and huge within the first few days of learning. But, the rate of memory loss decreases and the rate of much forgetting are much slower from then on.

Ebbinghaus also discovered another phenomenon called overlearning during his study on forgetting curve. The basic idea is that if you practiced something more than what is usually required to memorize it, the effect of overlearning takes place. This means that the information is now stored much more strongly and thus the effects of forgetting curve for overlearned information is shallower.

Rate of Forgetting

There are various factors that can affect the rate of forgetting. Some of which are

• Meaningfulness of the information
• The way it is represented
• Physiological actors (stress, sleep, etc)

The rate of forgetting isn’t same between every one. Herman Ebbinghaus pointed out that different in memory performance between two different individuals can be explained by mnemonic representation skills.

Increasing Memory Strength

Ebbinghaus hypothesized that difference in memory strength between individuals could be somewhat triumphed over by simple training in mnemonic techniques. Two of the methods he asserted to be among the best ways to increase strength of memory are:

• Better memory representation (e.g. with mnemonic techniques)
• Repetition based on active recall (esp. spaced repetition)

He believed that each repetition in learning leads to increase in the interval for when the next repetition is required. It was later discovered that higher original learning also lead to slower loss in memory.

For instance , taking time to repeat information every day during exams decreases the effects of the forgetting curve. According to research, information should be repeated within the first 24 hours of learning to reduce the rate of memory loss.

Note: Not all memories follow the forgetting curve as there could be various other factors in play, such as noise and other environmental factors. Because of their influence on what information is remembered, not all memories are affected by detrimental effects of interference.

Critical Evaluation of the Hypothesis

The greatest debate regarding the forgetting curve is about the shape of the forgetting curve when it comes to more significant notable events. Some researchers suggest that memories of shocking events like 9/11 attack, Boston bombing, etc are imprinted in our memory (flash bulb memory).

Others, however, have made an argument that recollections of events recorded in different years have shown sizeable amount of variations.

There have been extensive amount of research in this particular subject matter as it closely relates to eyewitness identification testimony. But, so far, it’s been noted that information gained from eye witness accounts are demonstrably unreliable.

What are other people reading?

Motivated Forgetting

Proactive Interference

The rapidity of forgetting indicated by this curve is remarkable, and combating its effects should be a top priority for any student and educator, whether they are in an offline or online learning environment. However, not all forgetting is bad. In fact, forgetting has an important role in human memory in that it leads to a higher rate of retention in the long term. Forgetting bits of information and then recalling them is a much more effective strategy than re-reading the same information multiple times and attempting to “solidify” the knowledge in that way.

That is why in the “How to combat the curve of forgetting” section of this article, I’ll be covering tips on how to achieve a delicate state of balance between memorization and forgetting. With these tips, you can turn your forgetfulness into a superpower. In addition to that, I’ll be going over the nitty-gritty of the Ebbinghaus forgetting curve: what it is and who’s the man behind it.

In this guide, you’ll learn the following:

The Ebbinghaus Forgetting Curve

What is the ebbinghaus forgetting curve.

The Ebbinghaus forgetting curve is a graph that shows the rate at which human memory deteriorates over time. It is characterized by memories going through an initial stage of rapid memory decline within 24 hours, followed by a slower rate of memory decay over the long term.

This curve of forgetting shows us that:

• Forgetting is a natural and necessary part of life.
• Avoiding forgetting altogether is a never-ending uphill battle due to the human brain’s natural tendency to forget things.
• Nearly all our memories of a given event are lost within the first 24 hours.
• Learning methods and learning frequency play a role in the memory decay rate.
• Avoiding memory trace decay is best done by learning through repetitions over long periods of time rather than using “cramming.”

The forgetting curve in memory can be offset by a number of factors and techniques, such as  spaced repetition ,  active recall , and other memory techniques, such as Leitner’s flashcard system . These methods can increase your rates of long-term memory retention by regularly forcing you to recall forgotten facts and concepts.

The formula with which the Ebbinghaus curve of forgetting is calculated is the following:  R = exp(-t/S) , with R symbolizing memory retention, t symbolizing time, and S the relative strength of the memory.

Who was Hermann Ebbinghaus?

Hermann Ebbinghaus was a German psychologist and philosopher most well-known for his research on the forgetting curve and the spacing effect. His groundbreaking work on the forgetting curve was published in his paper “ Memory: A Contribution to Experimental Psychology ” in 1885.

Now, how did he arrive at his memory theory?

Hermann Ebbinghaus gained an interest in psychology while studying history and philology at the University of Bonn, and he completed his Ph.D. in philosophy in 1873, after which he founded some of Europe’s first psychological testing laboratories in Germany and Poland.

Hermann Ebbinghaus is considered one of the fathers of experimental psychology due to his first-in-history systematic study of memory, published in 1885. He conducted several experiments on human learning and forgetting that led him to conclude that forgetting over long periods is proportional to how often something has been remembered previously. This discovery meant that it’s not just about how much information you’re exposed to, but it’s also important how frequently these memories are recalled again.

Not only did the work of Ebbinghaus inspire future generations of memory researchers, but he also campaigned for additional funding for memory research and supported scientific research in psychology at a time when psychology was seen as a sort of pseudoscience by many scientists.

How to combat the curve of forgetting

Here are five ways you can combat the curve of forgetting:

Choose to use active recall instead of a passive review

Use spaced repetition to achieve better memory retention, use mnemonics to combat the curve of forgetting, make the information relevant to yourself, add an element of social interaction.

Active recall refers to reviewing materials in an active and self-directed manner with the help of tools such as flashcards or self-quizzes. This learning style is vastly different from conducting a passive review in which the learner simply reads the information without trying to actively recall what they’ve learned. Active recall forces learners to actively participate in the learning process and to think about the material in a deeper way, forming stronger memories as a result. 

By using active recall, you will make sure that the information has a better chance of sticking in your long-term memory. You’ll also be able to retain more of what you’ve learned because there will be new neural connections formed due to your critical thinking skills being more engaged.

Practicing active recall instead of passive reviewing is beneficial for combating the forgetting curve because it:

• Results in  deeper and stronger memory traces .
• Self-testing yourself  once  achieves better results than rereading information four times.
• Forces you to learn without any guidance, thus  improving long-term retention  and exam test results.

Now, let’s go over another closely related learning concept – spaced repetition.

Spaced repetition refers to the practice of repeating and recalling information at specific time intervals. When you review information in a given interval, it’s easier to remember because each successive review goes over the same concepts over and over. This process reduces forgetting, improves retention over time, and takes advantage of the Ebbinghaus forgetting curve. 

The Ebbinghaus forgetting curve demonstrates that as you go through repetitions of the information, your recall of it gets better. At first, training is not effective because there are many new items to remember. However, as time passes and more words build on each other through repetition, they become simpler to recall because the neural pathways in your brain get stronger.

The spaced repetition memory technique is based on this concept. If you use the spaced repetition technique, you’re constantly recalling the same information and combating the onset of the curve of forgetting. The spaced repetition technique can be especially powerful with modern tools such as Anki or SuperMemo, which use powerful algorithms to train your memory in the most optimized ways possible.

Hundreds of studies  have highlighted the benefits of spaced repetition, and these benefits include:

• Continuously re-exposing your brain to the same information at various intervals.
• Developing  stronger memory traces  through repeated recall.
• Repeated and consistent learning in short study sessions produces less anxiety for exams.

To make the information more relevant and engaging, consider using mnemonics to combat the curve of forgetting. The use of mnemonics in learning dates back all the way to  477 BCE , and they are still just as effective as ever. Using mnemonics helps you remember more by associating the learned information with a word, phrase, or sentence which you already have in your long-term memory.

As  an example , picture yourself in geography class, trying to memorize the differences between “longitude” and “latitude.” Here, mnemonics can be of great help. If you look at a globe, the lines that run across North and South are long, and these lines also symbolize “longitude.” Thus, to remember the direction of longitude, you could simply remember that the longitudinal lines are long.

Simon Reinhard , a world record-holding memory athlete capable of memorizing the order of an entire deck of cards in less than half a minute, also heavily relies on using mnemonics to remember things better.

Making connections in your brain with the use of mnemonics can help you:

• Create stronger memory traces.
• Helps  encode information into long-term memory .
• Learn  large amounts of information  and ordered lists with relative ease.

There’s another effective technique to make learning and memorizing easier – making it relevant to yourself. The more relevant the information is, the easier it is for your brain to make a connection with what you already know and thus allow you to remember it. By connecting our own personal experiences with the topic, we can enhance the storing capacities of our brains and enhance memory trace retention.

There are three ways to make information more relevant:

• Connecting new information with what you already know.
• Making the new material sound as interesting or important to you as possible.
• Framing the new knowledge with your personal goals and experiences.

Besides relatedness and relevancy, there’s another effective technique to combat the curve of forgetting: social interactivity.

Our memories get stronger when we are actively involved in the learning process rather than being passive observers. This element of social interactivity is crucial in combating the forgetting curve because it strengthens the neurons in our brains and makes them more likely to establish long-lasting memory traces.

Participating in social interactions while going over the learning materials can help create a productive environment for memory encoding. And not only is social interaction important for the creation of memories, but it also enhances memory recall. However, for socially interactive learning to be effective, some of its disadvantages, such as ease of distraction and a lack of accountability, have to be taken into consideration.

Here are a few ways to make learning materials more socially interactive:

• Creating active learning environments such as “flipped classrooms” where students are actively involved in solving problems rather than passively listening to lectures.
• Working in small groups to solve problems and share knowledge.
• Doing hands-on experiments and solving real-world challenges.
• Participating in online forum discussions and learner communities.

In conclusion: Why is the curve of forgetting important?

In conclusion, the Ebbinghaus forgetting curve helps us to better understand the forgetting patterns of our memories. It shows that we tend to forget things very quickly but also that the effect can be combated through various methods such as spaced repetition, active recall, and mnemonics.

Human memory is a complex mechanism that can work against us as we age, but it’s also a mechanism that can be optimized and improved with the help of specific strategies. We can take an active role in improving our memory retention, and while it’s not always easy, it’s more than worth going the extra mile.

Through his work, Hermann Ebbinghaus helped us to better understand how our memories work, and for that reason, he will always be considered one of the pioneers of memory research. If you’d like to read more about the past, present, and future of learning research, make sure to read some of our other posts on study skills .

Sander Tamm

The Q/E/C Method of Note-Taking: The Key to Understanding the Big Picture

In this guide, I’ll show you when and how to use the Question/Evidence/Conclusion note-taking method for improving your understanding of the information presented to you in class.

Chris Voss MasterClass Review: Worth it or Not?

This is my honest review of the Chris Voss MasterClass after finally pulling the trigger and going through the online course.​

The 13 Most Effective Note-Taking Methods

These are efficient note-taking methods that anyone can pick up and use to take better notes.

3 minute read

Hermann Ebbinghaus

1850-1909 German psychologist whose work resulted in the development of scientifically reliable experimental methods for the quantitative measurement of rote learning and memory.

Hermann Ebbinghaus ( Corbis-Bettmann . Reproduced with permission.)

Born in Germany, Hermann Ebbinghaus received his formal education at the universities of Halle, Berlin, and Bonn, where he earned degrees in philosophy and history. After obtaining his philosophy degree in 1873, Ebbinghaus served in the Franco-Prussian War. For the next seven years following the war, he tutored and studied independently in Berlin, France, and England. In the late 1870s, Ebbinghaus became interested in the workings of human memory . In spite of Wilhelm Wundt 's assertion in his newly published Physiological Psychology that memory could not be studied experimentally, Ebbinghaus decided to attempt such a study, applying to this new field the same sort of mathematical treatment that Gustav Fechner (1801-1887) had described in Elements of Psychophysics (1860) in connection with his study of sensation and perception .

Using himself as both sole experimenter and subject, Ebbinghaus embarked on an arduous process that involved repeatedly testing his memorization of nonsense words devised to eliminate variables caused by prior familiarity with the material being memorized. He created 2,300 one-syllable consonant-vowel-consonant combinations—such as taz , bok , and lef— to facilitate his study of learning independent of meaning. He divided syllables into a series of lists that he memorized under fixed conditions. Recording the average amount of time it took him to memorize these lists perfectly, he then varied the conditions to arrive at observations about the effects of such variables as speed, list length, and number of repetitions. He also studied the factors involved in retention of the memorized material, comparing the initial memorization time with the time needed for a second memorization of the same material after a given period of time (such as 24 hours) and subsequent memorization attempts. These results showed the existence of a regular forgetting curve over time that approximated a mathematical function similar to that in Fechner's study. After a steep initial decline in learning time between the first and second memorization, the curve leveled off progressively with subsequent efforts.

Ebbinghaus also measured immediate memory, showing that a subject could generally remember between six and eight items after an initial look at one of his lists. In addition, he studied comparative learning rates for meaningful and meaningless material, concluding that meaningful items, such as words and sentences, could be learned much more efficiently than nonsense syllables. His experiments also yielded observations about the value of evenly spaced as opposed to massed memorization. A monumental amount of time and effort went into this ground-breaking research. For example, to determine the effects of number of repetitions on retention, Ebbinghaus tested himself on 420 lists of 16 syllables 340 times each, for a total of 14,280 trials. After careful accumulation and analysis of data, Ebbinghaus published the results of his research in the volume On Memory in 1885, while on the faculty of the University of Berlin. Although Wundt argued that results obtained by using nonsense syllables had limited applicability to the actual memorization of meaningful material, Ebbinghaus's work has been widely used as a model for research on human verbal learning, and Über Gedachtnis ( On Memory) has remained one of the most cited and highly respected sourcebooks in the history of psychology.

In 1894, Ebbinghaus joined the faculty of the University of Breslau. While studying the mental capacities of children in 1897, he began developing a sentence completion test that is still widely used in the measurement of intelligence . This test, which he worked on until 1905, was probably the first successful test of mental ability . Ebbinghaus also served on the faculties of the Friedrich Wilhelm University and the University of Halle. He was a cofounder of the first German psychology journal, the Journal of Psychology and Physiology of the Sense Organs, in 1890, and also wrote two successful textbooks, The Principles of Psychology (1902) and A Summary of Psychology (1908), both of which went into several editions. His achievements represented a major advance for psychology as a distinct scientific discipline and many of his methods continue to be followed in verbal learning research.

See also Forgetting curve ; Intelligence quotient

Additional topics

• Hermann Von Helmholtz
• Henry Alexander Murray Jr. - Becomes a physician and researcher, Discovers psychoanalysis and "depth psychology"
• Other Free Encyclopedias

Psychology Encyclopedia Famous Psychologists & Scientists

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• My Bibliography
• Collections
• Citation manager

Save citation to file

Email citation, add to collections.

• Create a new collection
• Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

• Search in PubMed
• Search in NLM Catalog
• Add to Search

Replication and Analysis of Ebbinghaus' Forgetting Curve

Affiliation.

• 1 University of Amsterdam, Amsterdam, The Netherlands.
• PMID: 26148023
• PMCID: PMC4492928
• DOI: 10.1371/journal.pone.0120644

We present a successful replication of Ebbinghaus' classic forgetting curve from 1880 based on the method of savings. One subject spent 70 hours learning lists and relearning them after 20 min, 1 hour, 9 hours, 1 day, 2 days, or 31 days. The results are similar to Ebbinghaus' original data. We analyze the effects of serial position on forgetting and investigate what mathematical equations present a good fit to the Ebbinghaus forgetting curve and its replications. We conclude that the Ebbinghaus forgetting curve has indeed been replicated and that it is not completely smooth but most probably shows a jump upwards starting at the 24 hour data point.

PubMed Disclaimer

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Fig 1. Learning schedule during 2011–2012 for…

Fig 1. Learning schedule during 2011–2012 for all lists, where labels in bold indicate when…

Fig 2. Four loglog graphs with savings…

Fig 2. Four loglog graphs with savings as a function of retention interval with fitted…

Fig 3. Four log graphs with savings…

Fig 3. Four log graphs with savings as a function of retention interval with best-fitting…

Fig 4. Normalized savings scores as a…

Fig 4. Normalized savings scores as a function of retention interval on a logarithmic scale,…

Fig 5. Learning time per list as…

Fig 5. Learning time per list as a function of day of experiment with a…

Fig 6. Serial position for correct relearning…

Fig 6. Serial position for correct relearning scores for each retention interval and for the…

Fig 7. Proportion correct as a function…

Fig 7. Proportion correct as a function of retention interval on a logarithmic scale.

Similar articles

• Why Ebbinghaus' savings method from 1885 is a very 'pure' measure of memory performance. Murre JMJ, Chessa AG. Murre JMJ, et al. Psychon Bull Rev. 2023 Feb;30(1):303-307. doi: 10.3758/s13423-022-02172-3. Epub 2022 Sep 7. Psychon Bull Rev. 2023. PMID: 36069971 Free PMC article.
• Biologically Plausible Artificial Synaptic Array: Replicating Ebbinghaus' Memory Curve with Selective Attention. Roe DG, Kim S, Choi YY, Woo H, Kang MS, Song YJ, Ahn JH, Lee Y, Cho JH. Roe DG, et al. Adv Mater. 2021 Apr;33(14):e2007782. doi: 10.1002/adma.202007782. Epub 2021 Feb 28. Adv Mater. 2021. PMID: 33644934
• Non-declarative sequence learning does not show savings in relearning. Keisler A, Willingham DT. Keisler A, et al. Hum Mov Sci. 2007 Apr;26(2):247-56. doi: 10.1016/j.humov.2007.01.003. Epub 2007 Mar 6. Hum Mov Sci. 2007. PMID: 17343944 Free PMC article.
• Using Micro-learning on Mobile Applications to Increase Knowledge Retention and Work Performance: A Review of Literature. Shail MS. Shail MS. Cureus. 2019 Aug 2;11(8):e5307. doi: 10.7759/cureus.5307. Cureus. 2019. PMID: 31511813 Free PMC article. Review.
• [Forgetting : theories and potential mechanisms]. Mansuy IM. Mansuy IM. Med Sci (Paris). 2005 Jan;21(1):83-8. doi: 10.1051/medsci/200521183. Med Sci (Paris). 2005. PMID: 15639026 Review. French.
• Enhancing cosmetic suturing skill acquisition in surgical residents through spaced learning training: a randomized controlled trial. Yan X, Abudouresuli A, Yuemaier A, Ge Y, Shang S, Yang J, Zhang L. Yan X, et al. Ann Med. 2024 Dec;56(1):2363940. doi: 10.1080/07853890.2024.2363940. Epub 2024 Aug 30. Ann Med. 2024. PMID: 39212199 Free PMC article. Clinical Trial.
• Teaching Pterygopalatine Fossa Anatomy Using 3D Images and Physical Model: A Novel Teaching Strategy. Narayanan S, Adikesavan PN, Ananthy V. Narayanan S, et al. Med Sci Educ. 2024 May 7;34(4):901-908. doi: 10.1007/s40670-024-02063-3. eCollection 2024 Aug. Med Sci Educ. 2024. PMID: 39099859
• An adapted white-coat and warm-heart intervention on nurses' knowledge, general stigmatizing attitudes, and work avoidance behaviors towards HIV: a quasi-experimental study. Yang M, Zhang L, Jiang Y, Liu P, Liu W, Cao X, Luo Q, Fu C, He L. Yang M, et al. J Occup Health. 2024 Jan 4;66(1):uiae041. doi: 10.1093/joccuh/uiae041. J Occup Health. 2024. PMID: 39038083 Free PMC article. Clinical Trial.
• An observer tool to enhance learning of medical students during simulation training of cardiopulmonary resuscitation: a randomised controlled trial. Goulamhoussen A, Havard C, Gille B, François B, Benhamou D, Blanié A. Goulamhoussen A, et al. BMC Med Educ. 2024 Jul 3;24(1):719. doi: 10.1186/s12909-024-05658-x. BMC Med Educ. 2024. PMID: 38961381 Free PMC article. Clinical Trial.
• Episodic long-term memory formation during slow-wave sleep. Schmidig FJ, Ruch S, Henke K. Schmidig FJ, et al. Elife. 2024 Apr 25;12:RP89601. doi: 10.7554/eLife.89601. Elife. 2024. PMID: 38661727 Free PMC article.
• Anderson JR, Schooler LJ (1991) Reflections of the environment in memory. Psychological Science 2: 396–408.
• Rubin DC, Wenzel AE (1996) One hundred years of forgetting: A quantitative description of retention. Psychological Review 103: 734–760.
• Wixted JT, Carpenter SK (2007) The Wickelgren Power Law and the Ebbinghaus Savings Function. Psychological Science 18: 133–134. - PubMed
• Wixted JT, Ebbesen EB (1991) On the form of forgetting. Psychological Science 2: 409–415.
• Bartlett FC (1932) Rembering. Cambridge: Cambridge University Press.
• Search in MeSH

Related information

Grants and funding, linkout - more resources, full text sources.

• Europe PubMed Central
• PubMed Central
• Public Library of Science

Other Literature Sources

• The Lens - Patent Citations
• scite Smart Citations
• MedlinePlus Health Information

• Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

The forgetting curve: the science of how fast we forget

Anne-Laure Le Cunff

We spend a lot of time reading and studying in the hope of acquiring new knowledge. However, most of us focus a lot more on learning rather than remembering. As a result, our mind is often a leaky bucket we keep on trying to fill, only for all of that new knowledge to soon disappear. This is because knowledge does not only have a learning curve. It also has a forgetting curve.

The Ebbinghaus forgetting curve

Hermann Ebbinghaus was a German psychologist who is known as a pioneer in the experimental study of memory. Curious about why we forget things and how to prevent it, he decided to run a study on himself. From 1880 to 1885, Ebbinghaus tried to commit words to memory, and repeatedly tested himself after various time periods, and recorded the results. The words were nonsense combinations of syllables, following a Consonant–Vowel–Consonant pattern. He then plotted the results he obtained on a graph, which looked like this:

This graph is now known as the forgetting curve, which illustrates how information we learn is lost over time when we make no attempt to retain it. The forgetting curve suggests that we tend to halve our memory of new knowledge in a matter of days or weeks, unless we make a conscious effort to review the newly learned material.

Of course, such a study with just one subject is limited in nature, but Ebbinghaus is considered one of the first scientists to explore the subject of forgetting through well-designed experiments on the subject of forgetting, and his research contributed a lot to the field of experimental psychology.

In fact, Ebbinghaus’ findings are interesting enough for scientists to still explore the concept of the forgetting curve to this day. As you may know, psychology is a field where replication of studies is often a problem. However, in 2015, a research team successfully reproduced the forgetting curve from Ebbinghaus’ findings.

The researchers write: “In 1880, Ebbinghaus set new standards for psychology experiments, already incorporating such ‘modern’ concepts as controlled stimulus materials, counterbalancing of time-of-day effects, guarding against optional stopping, statistical data analysis, and modeling to find a concise mathematical description and further verify his results. The result was a high-quality forgetting curve that has rightfully remained a classic in the field. Replications, including ours, testify to the soundness of his results.”

Knowing that you will probably forget most of what you study in the absence of intentional attempts to retain information, how can you go about reducing your forgetting rate so you can remember more of what you learn?

How to reduce your forgetting rate

Be honest: how often have you read a book only to forget most of its content a few months later? How much do you remember of that article you thoroughly enjoyed reading last year? When people ask you what a podcast episode you recommended is about, are you able to give them a detailed summary, or only share a vague overview?

Most people have a high forgetting rate. The good news is, Ebbinghaus explored some ways to reduce that rate so your forgetting curve is not so steep.

• Build meaningful memories. The better you understand the information you want to remember, the easier it will be to recall that information. Ebbinghaus suggests fostering better memory representation by using mnemonic techniques , which are structured strategies to better memorise and remember things. The concept of mnemonic techniques is not new: as it was essential for orators to remember what they had to say when addressing a crowd, these techniques were already used by ancient Greeks and Romans to practice what they called the “Art of Memory”. They are still employed nowadays by memory champions .
• Use spaced repetition. Ebbinghaus found that repetition based on active recall, and especially spaced repetition, was practically helpful in reducing his forgetting rate. This is because of the spacing effect, which shows that much more information is encoded into your long-term memory — and better — when you avoid cramming everything you want to learn in one study session (which researchers call “mass practice”), and use spaced study sessions instead. The spacing effect has been extensively studied and is one of the few evidence-based learning strategies you can confidently rely on.
• Practice overlearning. Lastly, Ebbinghaus defined overlearning as the number of repetitions of information after which it can be recalled with perfect accuracy. Overlearning consists in reviewing newly acquired knowledge beyond the initial point of mastery. In a 1992 meta-analysis, researchers found that overlearning may indeed significantly affect recall over time. However, recent research suggests that the effects of overlearning tend to not last very long, so take this one with a grain of salt.

Keep in mind that, even though a lot of Ebbinghaus’ work has been reproduced, his own sample size was just himself and he used a very specific type of content to remember. Many differences in context, content, and individual abilities will impact the way we learn and remember. The forgetting curve should not be interpreted as a general graph that can be applied to everyone. Rather, it is an illustration of how we tend to rapidly forget the information we study if we don’t use it nor make any attempt to retain it.

There is no magic bullet to easily recall everything you learn about. Knowledge needs to be understood, then regularly used in order for you to remember it. This requires a conscious effort and a higher time commitment than just consuming content without any attempt to retain it. As such, be selective with what you want to remember, and make it as simple as possible by using the right tools .

Join 100,000 mindful makers!

Ness Labs is a weekly newsletter with science-based insights on creativity, mindful productivity, better thinking and lifelong learning.

One email a week, no spam, ever. See our Privacy policy .

Don’t work more. Work mindfully.

Ness Labs provides content, coaching, courses and community to help makers put their minds at work. Apply evidence-based strategies to your daily life, discover the latest in neuroscience research, and connect with fellow mindful makers.

Ness Labs © 2022. All rights reserved .

Hermann Ebbinghaus

The Online Museum on Forgetting

Scroll Down to Start

Hermann Ebbinghaus (1850-1909) was a German psychologist who made significant contributions to the field of psychology, particularly in the areas of memory and learning. Ebbinghaus was born in Barmen, Germany, on January 24, 1850. He grew up in a family of merchants and initially studied philosophy at the University of Bonn. However, he later turned his attention to psychology and became one of the first psychologists to conduct systematic experiments in the field.

Ebbinghaus is best known for his work on memory and learning. He conducted experiments on himself to study the nature of memory and developed a model of memory that is still widely used today. His research also laid the foundation for the study of human learning and memory, which is a critical area of research in psychology.

Ebbinghaus spent most of his academic career at the University of Berlin, where he became a professor of philosophy and psychology in 1894. He was a prolific writer and published numerous articles and books on psychology, including his seminal work, "Memory: A Contribution to Experimental Psychology."

Ebbinghaus was married to his wife, Lily, for over 30 years and had three children. He died on February 26, 1909, in Breslau, Germany, at the age of 59.

Ebbinghaus' legacy continues to be felt in the field of psychology today. His work on memory and learning has had a significant impact on the development of cognitive psychology and continues to influence research in the field.

The Science of Forgetting

The Psychological Science of 19th century

The 19th century saw the emergence of psychological science, which initially focused on the basic elements of consciousness. This approach, known as structuralism, aimed to identify the fundamental components of human experience through introspection. Structuralists sought to break down consciousness into its constituent parts, such as sensations and feelings. This emphasis on basic elements of consciousness marked the early years of psychology and set the stage for later theoretical and empirical advances. However, this approach was criticized for its limited scope and lack of practical applications, leading to the rise of more holistic perspectives and a growing emphasis on empirical methods.

The growing emphasis on empirical methods in psychology has been a hallmark of the discipline in recent decades. Empirical methods involve the use of experimental designs, quantitative techniques, and statistical analyses to test hypotheses and gather data. This approach has led to significant advances in our understanding of human behavior and mental processes, as well as a greater appreciation for the importance of evidence-based practice. However, despite these advances, there is still a limited understanding of the brain's function in mental processes. Ongoing research seeks to elucidate the neural mechanisms underlying cognitive and emotional processes, paving the way for new insights and potential interventions in the future.

A Simple Model, a Simple Measure

• Hermann Ebbinghaus pioneered the science of forgetting in the late 19th century, using a novel approach that laid the foundation for modern memory research. Ebbinghaus employed nonsense syllables, such as "WID" and "ZOF," to control for pre-existing associations in his study participants. He also used repeated learning and testing to track retention over time, calculating a savings score to quantify the amount of information retained. Additionally, Ebbinghaus systematically manipulated variables such as the length, spacing, and difficulty of the lists to determine their effects on memory. This quantitative approach involved statistical analysis of the data, marking a significant departure from earlier, more subjective methods of inquiry.
• Ebbinghaus's research on forgetting not only established the scientific study of memory but also had practical implications for education and training. His findings showed that the most effective learning occurs through spaced repetition, rather than massed practice, and that the rate of forgetting slows over time. These insights have since been corroborated by subsequent research, leading to the development of effective memory-enhancement techniques. Ebbinghaus's legacy thus endures, as his pioneering work continues to inform contemporary memory research and its applications in fields such as education, medicine, and technology.

Findings as Insightful Data

Forgetting curve: Ebbinghaus' forgetting curve describes how the amount of information retained decreases over time, with the steepest drop occurring immediately after learning.

Rapid forgetting: Ebbinghaus' research demonstrated that most forgetting occurs quickly after initial learning, and then tapers off over time. This finding highlights the importance of reviewing material soon after initial learning to consolidate it in long-term memory.

Relearning improves retention: Ebbinghaus found that relearning previously studied material helps to improve retention, as it reinforces connections between neurons in the brain.

Spacing effect: Ebbinghaus' research showed that spacing out study sessions over time, rather than cramming, improves long-term memory retention. This is known as the "spacing effect."

Learning time: Ebbinghaus also found that longer learning sessions generally improve retention, likely because they allow for more repetition and elaboration of the material.

Original Image from Wikimedia.org

Meaningful material retained better: Ebbinghaus' work also suggested that information that is personally meaningful or relevant to an individual is more likely to be retained in long-term memory.

Overlearning: Continuing to study beyond the point of mastery, known as overlearning, can help to strengthen memories and improve retention, according to Ebbinghaus' research.

M ental associations improve retention: Ebbinghaus also emphasized the importance of creating mental associations between new information and existing knowledge, as this can help to improve retention by facilitating recall.

Memory distinct from sensation and perception: Through his research, Ebbinghaus demonstrated that memory is a distinct cognitive process from sensation and perception, which was an important step in establishing memory research as a scientific discipline.

Qu antification of memory: Ebbinghaus' emphasis on using empirical methods and quantitative analysis to study memory helped to establish a scientific study of memory, and paved the way for future research in this field.

Hermann Ebbinghaus' groundbreaking research on memory and forgetting continues to inform current scientific understanding of memory processes. His methods, such as the "forgetting curve" and the study of repetition, have been adapted for use in modern research, and his legacy will continue to shape future studies of memory and learning.

Primary Sources

Secondary Sources

Ebbinghaus, H. (1885). Über das Gedächtnis: Untersuchungen zur experimentellen Psychologie [On Memory: Investigations in Experimental Psychology]. Duncker & Humblot.

Ebbinghaus, H. (1897). Grundzüge der Psychologie [Outlines of Psychology]. Veit & Comp.

Ebbinghaus, H. (1908). Psychologie [Psychology]. Verlag von Veit & Comp.

Ebbinghaus, H. (1913). Memory: A Contribution to Experimental Psychology. Teachers College, Columbia University.

Ebbinghaus, H. (1964). On Memory and Forgetting. Dover Publications.

Benjamin, L. T. (1997). A History of Psychology: Original Sources and Contemporary Research. McGraw-Hill.

Boring, E. G. (1929). A history of experimental psychology. Appleton-Century.

Colvin, M. K. (1993). Cognitive Psychology: An Overview for Cognitive Scientists. Lawrence Erlbaum Associates.

Fancher, R. E. (1990). Pioneers of Psychology (3rd ed.). New York: Norton.

Kimble, G. A., & Perlmuter, L. C. (1970). Readings in the Psychology of Learning. Appleton-Century-Crofts.

Roediger, H. L. (1980). Memory research in the 20th century: A tribute to Hermann Ebbinghaus. American Psychologist, 35(9), 805-812.

Wozniak, R. H. (1999). Hermann Ebbinghaus (1850–1909). In M. A. Runco & S. R. Pritzker (Eds.), Encyclopedia of Creativity (Vol. 1, pp. 585-590). Academic Press.

The Global Brain Museum

FENS stands for the Federation of European Neuroscience Societies. It is a non-profit organization that represents and promotes neuroscience research in Europe. FENS supports scientific meetings, workshops, and training programs, and also provides a platform for communication and collaboration among neuroscientists in Europe and beyond.

The FENS' Brain Online Museum feature exhibits on brain sciences, neuroscience research, and the history of neuroscience, among other topics. It aims to create a virtual museum dedicated to the brain and also provide a platform for sharing neuroscience-related educational resources and promoting public understanding of neuroscience. This project on Ebbinghaus is funded and supported by FENS History of Neuroscience Online Award, 2019.

You can visit the Museum by clicking here.

for visiting us.

Follow us and share the content at your Social Networks

Created by Luiz Henrique Santana, Ph.D. and Marcelo Costa, Ph.D.

• Learning & Development

What is the Ebbinghaus forgetting curve? Examples and strategies for overcoming it

• Posted by by Athena Marousis
• March 30, 2023
• 5 minute read

It’s one of the most common problems that every company faces, regardless of industry or size— how do you help employees remember and apply the training they’ve received? No matter how engaging or impactful your training is, people just can’t remember all of it as time goes by. This phenomenon is called the Ebbinghaus forgetting curve, named for the psychologist to first identify it.

Discover what it is, and most importantly, how you can help yourself and your teams fight it.

What is the forgetting curve?

Illustrating ebbinghaus’s forgetting curve, ebbinghaus forgetting curve examples, overcoming the forgetting curve, key takeaways.

The terms “Ebbinghaus forgetting curve definition’ and ‘forgetting curve psychology definition’ both refer to the rapid decline in memory retention over time . Identified by German psychologist Hermann Ebbinghaus in the 1880s , he found that within an hour of learning new information people tend to forget up to 50% of it. Within 24 hours, this can increase to 70%. By the end of the week, people tend to retain only about 25% of what they’ve learned. This rapid forgetting curve can have serious implications for individuals and businesses alike, which is why it is important to understand how it works and how to overcome it.

Ebbinghaus and the forgetting curve— why it matters

Ebbinghaus’s groundbreaking research on the forgetting curve remains relevant and important today, nearly 200 years after it was conducted. By studying memory retention, he was able to identify the factors that contribute to forgetting and to develop strategies for overcoming it. One of his key findings was that repetition and reinforcement are critical for retaining information over time.

Illustrating Ebbinghaus’s forgetting curve, studies have shown that, without reinforcement, people tend to forget up to 90% of what they’ve learned within a month . This underscores the importance of companies and training managers developing effective strategies for retention and reinforcement in order to ensure that learning and development efforts are not wasted.

Here you can see a visual representation of the forgetting curve, showing that the decline in memory is very steep in the first hours and days of learning something new, and then levels off as time goes on. A survey of 600 frontline workers conducted by TalentCards showed that people actually underestimate how much they forget, and that the forgetting curve is steeper than we realize .  

When asked if they remembered at least 50% of their training 30 days after completing it, 69% said yes. 54% said they could remember more than half of their training even after 6 months of completing it. The reality is that by this point, the average person will remember less than 20% of what they learned if they haven’t taken the time to reinforce their knowledge .

Here are four common examples where you’ve probably experienced the forgetting curve first hand:

• Cramming for an exam : If you cram for an exam the night before, you may remember the information well enough to pass the test the next day, but within a week, you are likely to have forgotten much of what you learned.
• Learning a new language: If you are not consistently practicing and using the new language you are trying to learn, you’ll find you won’t remember it the moment you need it, no matter how well you could speak in the past.
• Remembering names: If you meet someone and don’t make an effort to remember their name at that moment, you’ll probably have forgotten it within the hour.
• Memorizing phone numbers: You may remember memorizing phone numbers by “chunking” them into three digits, the next three digits, and then the final four. But in the age of smartphones, you simply tap the contact you want to call and those phone numbers you memorized in the past may no longer be in your memory.

Helping employees overcome the forgetting curve in the context of their work can be a challenge. Employees may go through training, but without reinforcement, much of the information will be lost over time. 

Fortunately, there are steps organizations can take to combat the forgetting curve and to understand how information processing theory plays a role in your training initiatives. Here are four tips to help employees overcome the forgetting curve:

Leverage the power of spaced learning

Spaced learning is a technique where information is reinforced at regular intervals to help with retention. In practice, this means that training should be reinforced on a regular basis to ensure that the information is remembered . Rather than simply providing training once and expecting employees to remember it forever, organizations should schedule regular refresher training and follow-up sessions to reinforce key concepts.

Create microlearning versions of long courses

Employees may not have the time or patience to review long courses on a regular basis. To make it easier for them to review key concepts, you can create microlearning versions of long courses. These shorter versions cover the most important points and are much easier and quicker for employees to review . By breaking down training into bite-sized pieces and using the chunking memory strategy , employees are more likely to review it regularly.

Ensure training material is easy to access

If training material is a pain to locate and access, employees are less likely to review it. To combat this issue, you should try to ensure that training material is mobile accessible . By providing easy access to training materials on employees’ mobile devices, you can make it more convenient for employees to review the material during their free time, and therefore more likely that they will.

Reward employees who regularly review training

Employees who take the time to review training material regularly are more likely to retain key information. To encourage this positive behavior, have a training platform in place that allows managers to see who is logging in and spending time reviewing their material, and passing assessments. By recognizing and rewarding employees who regularly review training, you’ll reinforce the importance of ongoing learning and development among your team.

Help employees fight the forgetting curve with a powerful microlearning app

What did Ebbinghaus say about forgetting?

Ebbinghaus’ showed that we forget information at a rapid rate soon after learning it, with the majority of forgetting occurring within the first few days. He also found that forgetting continues at a slower rate over time. These insights led to the development of the forgetting curve, which is a graphical representation of how we forget information over time.

What did Ebbinghaus discover about the learning curve?

While Ebbinghaus discovered that the majority of forgetting happens in the first few days after learning something new, it can be combated by using strategies such as spaced learning and active recall, to help flatten the learning curve.

How do you use the Ebbinghaus forgetting curve?

The Ebbinghaus forgetting curve can be used as a guide to improve learning and memory retention. You can fight the forgetting curve with these strategies:

• Space out learning sessions
• Use active recall
• Repeat the information
• Use mnemonic devices
• Practice regularly

What were Ebbinghaus’s key findings?

• The forgetting curve : Ebbinghaus discovered that forgetting occurs at a rapid rate soon after learning, with the majority of forgetting occurring within the first few days. However, after this initial drop in the recall, forgetting continues at a slower rate over time.
• Spacing effect : Ebbinghaus found that spacing out learning sessions over time improves memory retention compared to massed practice (cramming).
• Overlearning : Ebbinghaus discovered that overlearning (repeated practice beyond mastery) could help improve the long-term retention of information.
• Serial position effect : Ebbinghaus found that the position of information in a list (primacy or recency) affects how well it is remembered.
• Forgetting is affected by interference : Ebbinghaus found that forgetting is influenced by interference or the presence of other information that competes for attention.

Don’t forget about the forgetting curve

By recognizing the rapid rate at which we forget information soon after learning it, we can take steps to improve our memory retention and combat the effects of the forgetting curve. With strategies like spaced learning, active recall, repetition, mnemonic devices, and regular practice, you can prove employees’ ability to retain information over the long term.

• Employees tend to forget new information at a rapid rate, with much of the forgetting occurring in the first few hours and days after learning.
• To combat the forgetting curve, managers should encourage spaced learning and repetition of training material.
• Creating microlearning versions of long courses can make it easier for employees to review and retain important information.
• Making training materials mobile accessible can help employees review information on the go and reinforce learning.
• Rewarding employees who regularly review training material can help incentivize continuous learning and combat the effects of the forgetting curve

You might also like

• Utility Menu

GA4 Tracking Code

fa51e2b1dc8cca8f7467da564e77b5ea

• Make a Gift
• Join Our Email List
• How Memory Works

Memory is the ongoing process of information retention over time. Because it makes up the very framework through which we make sense of and take action within the present, its importance goes without saying. But how exactly does it work? And how can teachers apply a better understanding of its inner workings to their own teaching? In light of current research in cognitive science, the very, very short answer to these questions is that memory operates according to a "dual-process," where more unconscious, more routine thought processes (known as "System 1") interact with more conscious, more problem-based thought processes (known as "System 2"). At each of these two levels, in turn, there are the processes through which we "get information in" (encoding), how we hold on to it (storage), and and how we "get it back out" (retrieval or recall). With a basic understanding of how these elements of memory work together, teachers can maximize student learning by knowing how much new information to introduce, when to introduce it, and how to sequence assignments that will both reinforce the retention of facts (System 1) and build toward critical, creative thinking (System 2).

Dual-Process Theory

Think back to a time when you learned a new skill, such as driving a car, riding a bicycle, or reading. When you first learned this skill, performing it was an active process in which you analyzed and were acutely aware of every movement you made. Part of this analytical process also meant that you thought carefully about why you were doing what you were doing, to understand how these individual steps fit together as a comprehensive whole. However, as your ability improved, performing the skill stopped being a cognitively-demanding process, instead becoming more intuitive. As you continue to master the skill, you can perform other, at times more intellectually-demanding, tasks simultaneously. Due to your knowledge of this skill or process being unconscious, you could, for example, solve an unrelated complex problem or make an analytical decision while completing it.

In its simplest form, the scenario above is an example of what psychologists call dual-process theory. The term “dual-process” refers to the idea that some behaviors and cognitive processes (such as decision-making) are the products of two distinct cognitive processes, often called System 1 and System 2 (Kaufmann, 2011:443-445). While System 1 is characterized by automatic, unconscious thought, System 2 is characterized by effortful, analytical, intentional thought (Osman, 2004:989).

Dual-Process Theories and Learning

How do System 1 and System 2 thinking relate to teaching and learning? In an educational context, System 1 is associated with memorization and recall of information, while System 2 describes more analytical or critical thinking. Memory and recall, as a part of System 1 cognition, are focused on in the rest of these notes.

As mentioned above, System 1 is characterized by its fast, unconscious recall of previously-memorized information. Classroom activities that would draw heavily on System 1 include memorized multiplication tables, as well as multiple-choice exam questions that only need exact regurgitation from a source such as a textbook. These kinds of tasks do not require students to actively analyze what is being asked of them beyond reiterating memorized material. System 2 thinking becomes necessary when students are presented with activities and assignments that require them to provide a novel solution to a problem, engage in critical thinking, or apply a concept outside of the domain in which it was originally presented.  

It may be tempting to think of learning beyond the primary school level as being all about System 2, all the time. However, it’s important to keep in mind that successful System 2 thinking depends on a lot of System 1 thinking to operate. In other words, critical thinking requires a lot of memorized knowledge and intuitive, automatic judgments to be performed quickly and accurately.

How does Memory Work?

In its simplest form, memory refers to the continued process of information retention over time. It is an integral part of human cognition, since it allows individuals to recall and draw upon past events to frame their understanding of and behavior within the present. Memory also gives individuals a framework through which to make sense of the present and future. As such, memory plays a crucial role in teaching and learning. There are three main processes that characterize how memory works. These processes are encoding, storage, and retrieval (or recall).

• Encoding . Encoding refers to the process through which information is learned. That is, how information is taken in, understood, and altered to better support storage (which you will look at in Section 3.1.2). Information is usually encoded through one (or more) of four methods: (1) Visual encoding (how something looks); (2) acoustic encoding (how something sounds); (3) semantic encoding (what something means); and (4) tactile encoding (how something feels). While information typically enters the memory system through one of these modes, the form in which this information is stored may differ from its original, encoded form (Brown, Roediger, & McDaniel, 2014).

• Retrieval . As indicated above, retrieval is the process through which individuals access stored information. Due to their differences, information stored in STM and LTM are retrieved differently. While STM is retrieved in the order in which it is stored (for example, a sequential list of numbers), LTM is retrieved through association (for example, remembering where you parked your car by returning to the entrance through which you accessed a shopping mall) (Roediger & McDermott, 1995).

Improving Recall

Retrieval is subject to error, because it can reflect a reconstruction of memory. This reconstruction becomes necessary when stored information is lost over time due to decayed retention. In 1885, Hermann Ebbinghaus conducted an experiment in which he tested how well individuals remembered a list of nonsense syllables over increasingly longer periods of time. Using the results of his experiment, he created what is now known as the “Ebbinghaus Forgetting Curve” (Schaefer, 2015).

Through his research, Ebbinghaus concluded that the rate at which your memory (of recently learned information) decays depends both on the time that has elapsed following your learning experience as well as how strong your memory is. Some degree of memory decay is inevitable, so, as an educator, how do you reduce the scope of this memory loss? The following sections answer this question by looking at how to improve recall within a learning environment, through various teaching and learning techniques.

As a teacher, it is important to be aware of techniques that you can use to promote better retention and recall among your students. Three such techniques are the testing effect, spacing, and interleaving.

• The testing effect . In most traditional educational settings, tests are normally considered to be a method of periodic but infrequent assessment that can help a teacher understand how well their students have learned the material at hand. However, modern research in psychology suggests that frequent, small tests are also one of the best ways to learn in the first place. The testing effect refers to the process of actively and frequently testing memory retention when learning new information. By encouraging students to regularly recall information they have recently learned, you are helping them to retain that information in long-term memory, which they can draw upon at a later stage of the learning experience (Brown, Roediger, & McDaniel, 2014). As secondary benefits, frequent testing allows both the teacher and the student to keep track of what a student has learned about a topic, and what they need to revise for retention purposes. Frequent testing can occur at any point in the learning process. For example, at the end of a lecture or seminar, you could give your students a brief, low-stakes quiz or free-response question asking them to remember what they learned that day, or the day before. This kind of quiz will not just tell you what your students are retaining, but will help them remember more than they would have otherwise.
• Spacing.  According to the spacing effect, when a student repeatedly learns and recalls information over a prolonged time span, they are more likely to retain that information. This is compared to learning (and attempting to retain) information in a short time span (for example, studying the day before an exam). As a teacher, you can foster this approach to studying in your students by structuring your learning experiences in the same way. For example, instead of introducing a new topic and its related concepts to students in one go, you can cover the topic in segments over multiple lessons (Brown, Roediger, & McDaniel, 2014).
• Interleaving.  The interleaving technique is another teaching and learning approach that was introduced as an alternative to a technique known as “blocking”. Blocking refers to when a student practices one skill or one topic at a time. Interleaving, on the other hand, is when students practice multiple related skills in the same session. This technique has proven to be more successful than the traditional blocking technique in various fields (Brown, Roediger, & McDaniel, 2014).

As useful as it is to know which techniques you can use, as a teacher, to improve student recall of information, it is also crucial for students to be aware of techniques they can use to improve their own recall. This section looks at four of these techniques: state-dependent memory, schemas, chunking, and deliberate practice.

• State-dependent memory . State-dependent memory refers to the idea that being in the same state in which you first learned information enables you to better remember said information. In this instance, “state” refers to an individual’s surroundings, as well as their mental and physical state at the time of learning (Weissenborn & Duka, 2000). 
• Schemas.  Schemas refer to the mental frameworks an individual creates to help them understand and organize new information. Schemas act as a cognitive “shortcut” in that they allow individuals to interpret new information quicker than when not using schemas. However, schemas may also prevent individuals from learning pertinent information that falls outside the scope of the schema that has been created. It is because of this that students should be encouraged to alter or reanalyze their schemas, when necessary, when they learn important information that may not confirm or align with their existing beliefs and conceptions of a topic.
• Chunking.  Chunking is the process of grouping pieces of information together to better facilitate retention. Instead of recalling each piece individually, individuals recall the entire group, and then can retrieve each item from that group more easily (Gobet et al., 2001).
• Deliberate practice.  The final technique that students can use to improve recall is deliberate practice. Simply put, deliberate practice refers to the act of deliberately and actively practicing a skill with the intention of improving understanding of and performance in said skill. By encouraging students to practice a skill continually and deliberately (for example, writing a well-structured essay), you will ensure better retention of that skill (Brown et al., 2014).

For more information...

Brown, P.C., Roediger, H.L. & McDaniel, M.A. 2014.  Make it stick: The science of successful learning . Cambridge, MA: Harvard University Press.

Gobet, F., Lane, P.C., Croker, S., Cheng, P.C., Jones, G., Oliver, I. & Pine, J.M. 2001. Chunking mechanisms in human learning.  Trends in Cognitive Sciences . 5(6):236-243.

Kaufman, S.B. 2011. Intelligence and the cognitive unconscious. In  The Cambridge handbook of intelligence . R.J. Sternberg & S.B. Kaufman, Eds. New York, NY: Cambridge University Press.

Osman, M. 2004. An evaluation of dual-process theories of reasoning. Psychonomic Bulletin & Review . 11(6):988-1010.

Roediger, H.L. & McDermott, K.B. 1995. Creating false memories: Remembering words not presented in lists.  Journal of Experimental Psychology: Learning, Memory, and Cognition . 21(4):803.

Schaefer, P. 2015. Why Google has forever changed the forgetting curve at work.

Weissenborn, R. & Duka, T. 2000. State-dependent effects of alcohol on explicit memory: The role of semantic associations.  Psychopharmacology . 149(1):98-106.

• Designing Your Course
• In the Classroom
• Getting Feedback
• Equitable & Inclusive Teaching
• Advising and Mentoring
• Teaching and Your Career
• Teaching Remotely
• Tools and Platforms
• Comprehending and Communicating Knowledge
• Motivation and Metacognition
• Promoting Engagement
• Bok Publications
• Other Resources Around Campus

• Join Mind Tools

Ebbinghaus's Forgetting Curve

Why we keep forgetting and what we can do about it.

© GettyImages Zbynek Pospisil

Ever get the feeling you're forgetting something? Boost your power of recall!

Forgetting can be infuriating, particularly when you're trying to learn a new skill or absorb vital information. When you can't recall the knowledge you need, stress can build and your confidence can take a knock. It may even lead to wasted time, missed opportunities, and costly mistakes.

But when you understand why you forget, you can take steps to prevent it, and make sure that what you learn, sticks!

In this article, we explore Ebbinghaus's Forgetting Curve, an enduring model that demonstrates how memories are lost over time and what we can do to reinforce the things that we learn, so that we can remember them more effectively.

Learn about the Forgetting Curve with our video and transcript   .

What Is the Forgetting Curve?

German psychologist Hermann Ebbinghaus wanted to understand more about why we forget things and how to prevent it. His research produced the Forgetting Curve – a visual representation of the way that learned information fades over time (see figure 1, below). [1]

Ebbinghaus experimented with his own ability to remember using a list of nonsense syllables, which he attempted to recall after different lengths of time. His experiences and results revealed a number of key aspects of memory:

• Memories weaken over time. If we learn something new, but then make no attempt to relearn that information, we remember less and less of it as the hours, days and weeks go by.
• The biggest drop in retention happens soon after learning. This is reflected by the steep fall at the start of the Forgetting Curve (see figure 1). Without reviewing or reinforcing our learning, our ability to retain the information plummets. For example, you may leave a webinar or meeting with your head full of new facts and figures, only to find that you can remember very little of it just hours later.
• It's easier to remember things that have meaning. Things with little or no meaning (like the nonsense syllables Ebbinghaus tried to learn) conform most closely to the Forgetting Curve. So, for instance, if you're listening to a talk on a subject that you don't really understand or have little interest in, you'll likely forget it faster than if it were on a subject that you found really engaging or exciting.
• The way something is presented affects learning. The same set of information can be made more or less memorable, depending on how well it's communicated. You'll likely find it easier to remember something that's been organized logically and presented clearly. But you may well forget that haphazard, scribbled shopping list!
• How you feel affects how well you remember. Ebbinghaus believed that physiological factors, such as stress and sleep , play a significant part in how well we retain information. Many people experience this as a vicious cycle – they feel stress, which makes it harder to remember, creating even more stress. There's also strong evidence to suggest that sleep can help our brains to sort and store information. [2]

Figure 1 – The Forgetting Curve

Ebbinghaus's research dates back to the 1880s, but it is still widely used and highly regarded. In 2015, a research team successfully reproduced his findings, and concluded that his methods and theories still held true. [3]

The Importance of Not Forgetting

Memory is important for our survival. Our brains are good at storing information that helps us to avoid physical or psychological harm.

We are particularly good at remembering the things that we need to know – details that are of vital importance to our survival. For example, foods we should avoid, pathways or areas we should stick to, and the people who are important in our lives. We also tend to remember experiences that trigger powerful emotions – such as surprise, fear, success, or relief – for longer.

But this means that many of the things that we want to learn (or that others need us to know) can drop out of our memory all too easily.

This is where the Forgetting Curve comes in!

Some aspects of memory can change with age. Your short-term memory may feel weaker, for example, and it can be more challenging to learn completely new things. But Ebbinghaus's work showed that sensible strategies and good self-care can help to keep your memory strong.

Aside from aging, a number of other physical and mental factors can alter how you think and learn. If you're ever worried about a change in your memory, be sure to seek medical advice.

How to Prevent Forgetting and Boost Your Memory

It's tempting to think that Ebbinghaus's work paints a bleak picture of learning. But it's not all negative. In fact, his research highlighted several things that we can do to retain information for longer. In this section, we look at four strategies you can use to improve your power of recall:

1. Use "Spaced Learning"

The most important discovery Ebbinghaus made was that, by reviewing new information at key moments on the Forgetting Curve, you can reduce the rate at which you forget it!

This approach is often referred to as "spaced learning" or "distributive practice." [4] (See figure 2, below.)

Figure 2 – Using Spaced Learning to Combat the Forgetting Curve

Even though our memory fades quickly, a review session soon after the original learning can improve it. This session should happen when recall has slipped significantly, but hasn't fallen so low that you're essentially starting over.

Reviewing and refreshing information regularly halts the Forgetting Curve. (In figure 2, the dotted part of each curve shows what would likely happen otherwise.) And, although forgetting starts again after each review session, it's slower than before. That's why each new curve shown in figure 2 is shallower than the last.

The gaps between your review sessions can be longer as time goes on. So, you might refresh your learning from a lecture the following day, then two days later, then after a week, then after 30 days… and you'll still know all the key information a month on! Reviewing information like this, at strategic points after you originally learned it, will stretch your recall and strengthen the memories encoded in your brain. You'll also discover any gaps that you need to focus on and relearn, if necessary.

Exactly how you time and space your review sessions will depend on a number of factors: the type of material you're learning, how much detail you need to know, and how long you want to keep it fresh in your mind. And, if other information disrupts or distracts you, you'll likely have to put in more work to keep your learning strong.

2. Overlearn

Another strategy Ebbinghaus explored was "overlearning" – that is, putting in more than the usual amount of effort when you learn something. He found that doing this improved retention, and slowed the steep drop seen on the Forgetting Curve.

He also pointed out that, by using certain memory strategies, we can improve our chances of retaining even hard-to-learn information.

See our article, Memory Improvement Techniques   , for a range of tried-and-true "mnemonic" techniques that can help you to improve your power of recall.

You may find our articles on Purposeful Practice   and the Conscious Competence Ladder   useful, too. Both of these techniques are designed to help reinforce learning over time.

Finding This Article Useful?

You can learn another 60 learning skills, like this, by joining the Mind Tools Club.

Subscribe to Our Newsletter

Receive new career skills every week, plus get our latest offers and a free downloadable Personal Development Plan workbook.

3. Make Information Meaningful

Do everything you can to make the material that you need to learn clear, relevant and purposeful, and establish a strong reason for retaining it. The more you know how something will benefit you in the long term, the more likely your memory will prioritize it.

Reducing distractions and other demands – known as your " cognitive load   " – should also help with this.

4. Keep Challenging Your Memory

If you come to review some information and discover gaps in your memory, don't despair! This is the most productive time for stretching your recall   . Learning done at this point will be all the stronger because of the mental challenge involved.

If you're imparting learning or information to an audience, or delivering training, make it as interactive as possible. Even just asking questions will encourage people to sort and strengthen the information in their minds.

The Forgetting Curve, or the Ebbinghaus Curve of Forgetting, is an influential memory model. It shows how learned information slips out of our memories over time – unless we take action to keep it there.

The steepest drop in memory happens quickly after learning, so it's important to revisit the information you've learned sooner rather than later. After that, regular reviews will help to reinforce it. But you can leave longer and longer gaps between these review sessions. This is known as "spaced learning."

Doing this will help to reinforce your learning and improve your power of recall, so that you can remember what you've learned in the long term. Other strategies you can use to improve your memory are: overlearning information, making what you want to learn meaningful, and challenging your memory regularly.

This site teaches you the skills you need for a happy and successful career; and this is just one of many tools and resources that you'll find here at Mind Tools. Subscribe to our free newsletter , or join the Mind Tools Club and really supercharge your career!

Rate this resource

The Mind Tools Club gives you exclusive tips and tools to boost your career - plus a friendly community and support from our career coaches! 

Comments (4)

• Over a month ago Sarah_H wrote Thanks for your comment BrentAAnders, You make a very important point about needing to make the content memorable in order to aid better recall. This is so important for MindTools content too - we always try to make it memorable, that's why we use a range of articles, videos, infographics etc. Thanks again for your comments. Sarah Mind Tools Coach
• Over a month ago BrentAAnders wrote Great article with lots of really good information. Some key aspects that aren't directly addressed but definitely tie in are boredom and making the content memorable. As an instructor it is vital to present content that is engaging, interesting, and relevant to the student. The student needs to fully see why the information is important and should be learned. Additionally, by using student-centered instructional methodologies the student is actively engaged in hands-on learning which created more emotional connections and greater ownership of the learned material. This then also aids in later recall.
• Over a month ago Sarah_H wrote Hi xbeat05, Thanks for your comments. I can certainly see how the immersive experience of learning a language in the country you're living in can help reinforce learning and improve the forgetting curve. In fact that's what I would need to do to learn a new language as I really struggle to learn new languages and too easily forget! Sarah Mind Tools Team

Please wait...