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Receive, Retain and Retrieve: Psychological and Neurobiological Perspectives on Memory Retrieval

• Regular Article
• Published: 04 February 2023
• Volume 58 , pages 303–318, ( 2024 )

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• Anisha Savarimuthu 1 &
• R. Joseph Ponniah   ORCID: orcid.org/0000-0002-0618-6788 1  

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Memory and learning are interdependent processes that involve encoding, storage, and retrieval. Especially memory retrieval is a fundamental cognitive ability to recall memory traces and update stored memory with new information. For effective memory retrieval and learning, the memory must be stabilized from short-term memory to long-term memory. Hence, it is necessary to understand the process of memory retention and retrieval that enhances the process of learning. Though previous cognitive neuroscience research has focused on memory acquisition and storage, the neurobiological mechanisms underlying memory retrieval and its role in learning are less understood. Therefore, this article offers the viewpoint that memory retrieval is essential for selecting, reactivating, stabilizing, and storing information in long-term memory. In arguing how memories are retrieved, consolidated, transmitted, and strengthened for the long term, the article will examine the psychological and neurobiological aspects of memory and learning with synaptic plasticity, long-term potentiation, genetic transcription, and theta oscillation in the brain.

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Savarimuthu, A., Ponniah, R.J. Receive, Retain and Retrieve: Psychological and Neurobiological Perspectives on Memory Retrieval. Integr. psych. behav. 58 , 303–318 (2024). https://doi.org/10.1007/s12124-023-09752-5

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DOI : https://doi.org/10.1007/s12124-023-09752-5

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ORIGINAL RESEARCH article

Predicting accuracy in eyewitness testimonies with memory retrieval effort and confidence.

• Department of Psychology, Stockholm University, Stockholm, Sweden

Evaluating eyewitness testimonies has proven a difficult task. Recent research, however, suggests that incorrect memories are more effortful to retrieve than correct memories, and confidence in a memory is based on retrieval effort. We aimed to replicate and extend these findings, adding retrieval latency as a predictor of memory accuracy. Participants watched a film sequence with a staged crime and were interviewed about its content. We then analyzed retrieval effort cues in witness responses. Results showed that incorrect memories included more “effort cues” than correct memories. While correct responses were produced faster than incorrect responses, delays in responses proved a better predictor of accuracy than response latency. Furthermore, participants were more confident in correct than incorrect responses, and the effort cues partially mediated this confidence-accuracy relation. In sum, the results support previous findings of a relationship between memory accuracy and objectively verifiable cues to retrieval effort.

Introduction

Eyewitness memories are often critical sources of information for investigating what happened during a criminal offense ( Wells et al., 2006 ). Although playing a central role in criminal investigations and decision-making, eyewitness evidence has often been found to be unreliable, and constitutes a major contributing factor behind wrongful convictions ( Garrett, 2011 ; Innocence project, 2018 ). Erroneous eyewitness reports are sometimes due to a witness’ deliberate lies about the target event (see DePaulo et al., 2003 ; Sporer and Schwandt, 2006 ; Vrij et al., 2017 ). Perhaps less obvious, and another major source of eyewitness error, is when a witness gives an honest report but remembers things incorrectly. While differentiating between sincere correct and incorrect memories may be critical to reaching valid judicial decisions, research has demonstrated that people have great difficulty in judging the accuracy of others’ memories ( Lindholm, 2005 , 2008a , b ). Despite its importance to the judicial process, relatively little research has examined the extent to which erroneous eyewitness memories may differ from those that are accurate. The present study attempts to provide insight into potential differences between honestly reported correct and incorrect verbal eyewitness testimonies. We do this by replicating and extending the research of Lindholm et al. (2018) , in which memory accuracy was found to be related to indicators of retrieval effort in witnesses’ responses.

Means to Judge Memory Accuracy: Reality Monitoring and Cue-Utilization

While confidence in our own memories is not a perfect predictor of accuracy, research shows a consistent positive relationship between confidence judgments and memory accuracy (e.g., Robinson and Johnson, 1996 ; Odinot and Wolters, 2006 ; Wixted and Wells, 2017 ). Reality monitoring ( Johnson and Raye, 1981 ) and cue-utilization ( Koriat, 1997 , 2006 ) are two major theories on how we make judgments of our own memories, that is, metamemory judgments. Both theories propose that we rely on indirect cues (i.e., heuristics) when assessing the veracity of our memory, rather than having a direct access to the memory’s strength (cf. Hart, 1965 ). Both theories have also inspired the development of methods for assessing the accuracy of others’ memories (e.g., Schooler et al., 1986 ; Sporer, 1997 ; Ackerman and Koriat, 2011 ). Reality monitoring theory (or “source monitoring”; Johnson et al., 1993 ) suggests that memories of real and imagined events differ in a set of attributes, and that people rely on these differences when determining the source of their memory. According to the theory, real memories include more contextual-, sensory-, and semantic information whereas imagined memories contain more references to cognitive operations. Reality monitoring can also be based on one’s prior knowledge and beliefs, such as judging a memory of a flying pig as imagined due to the knowledge that pigs cannot fly. Techniques using the reality monitoring framework have been developed to distinguish real from suggested memories (e.g., Schooler et al., 1986 ), and truth-tellers from liars (e.g., Sporer, 1997 ; Vrij, 2018 ). Since these techniques rely on patterns across several criteria in a testimony (e.g., sensory-, spatial-, time information, and clarity, etc.), they have primarily been used to determine the veracity of memories of entire events rather than of individual details from an event.

Similar to reality monitoring, the theory of cue-utilization ( Koriat, 1997 , 2006 ) suggests that people’s judgments of their own memories can be based on knowledge and beliefs about how memory works (information or theory-based), or on the experience derived during the retrieval process (experience-based). Experience-based judgments are mainly concerned with the memory processes per se , such as the ease with which the memory is retrieved, rather than, as within the reality monitoring framework, the content of the memory. While theory-based judgments within this framework are seen as derived from a deliberate application of one’s beliefs and theories about how memory works, experience-based judgments are derived on a more automatic basis from cues during the retrieval process. These cues give rise to a sense of experience from which the strength of the memory is estimated. Hence, a memory that comes to mind rapidly and easily would be experienced as a strong memory representation, and thus be judged as more accurate than one coming to mind more slowly.

Indeed, considerable evidence now attests to the notion that metamemory judgments, such as confidence, are strongly influenced by the ease and probability with which a to-be-remembered item is retrieved. For example, Kelley and Lindsay (1993) showed that manipulating how easy a memory is to retrieve affects how confident a person is that the memory is correct. In their study, participants were exposed to potential answers to general knowledge questions, which were either correct, incorrect but related, or incorrect and unrelated to the questions. When participants later took a test with the same questions, they were quicker to respond to, and more confident in answers they had been exposed to before, compared to non-exposed answers. This was true whether the answer was correct or incorrect, indicating the critical role of retrieval ease as a basis for their confidence judgments.

Predicting Memory Accuracy

The vast majority of studies on eyewitness accuracy have focused on measuring and improving the accuracy of eyewitness identification, that is, witnesses’ ability to correctly recognize a perpetrator in a group of foils and suspects (see Wells et al., 2006 ). In these studies on recognition judgments, a witness’ subjective confidence in his/her memory is the most extensively researched factor (for reviews, see Brewer and Weber, 2008 ; Roediger et al., 2012 ; Roediger and DeSoto, 2014 ; Wixted et al., 2015 ; Wixted and Wells, 2017 ). Although it has been a matter of some debate over the years, the now prevailing view is that there is a consistent positive, albeit not perfect, relationship between confidence and recognition accuracy ( Wixted et al., 2015 ; Wixted and Wells, 2017 ; see also Sporer et al., 1995 ; Juslin et al., 1996 ; Lindsay et al., 1998 ). Confidence has also been a prime interest in studies on verbal eyewitness recall, such as eyewitness testimony. While the strength of the relationship between confidence and accuracy in witness recall has varied somewhat throughout studies, the overall trend is consistent with, and mirrors the results of recognition studies; people are more confident in recalled memories that are correct, compared to incorrect ( Robinson and Johnson, 1996 ; Robinson et al., 1997 ; Ibabe and Sporer, 2004 ; Odinot and Wolters, 2006 ; Odinot et al., 2009 ).

As explained previously, the cue-utilization view proposes that confidence judgments are not directly derived from the strength of memories but are based on internal (experience-based judgments) and external cues (information-based judgments), which are presumably related to a memory’s accuracy. However, if confidence is based on cues and not the strength of the memory itself, then the cues may constitute a more direct and valid relation to a memory’s accuracy than does confidence. Moreover, while confidence may be based on the indirect accuracy of cues, it seems plausible that the cues people rely on are not always those that are the most accurate predictors. Hence, if cues to a memory’s strength can be identified and measured, then such cues may provide a better estimate of accuracy than confidence judgments.

One cue that has been found to predict both accuracy and confidence is response latency, that is, the speed with which a memory is produced. As shown by Kelley and Lindsay (1993) , people are more confident in quickly produced as compared to more slowly produced verbal responses. The same results were obtained in a study by Robinson et al. (1997) , in which participants answered questions about details from a video of a staged theft. Higher confidence and shorter response latency for correct answers was found both for verbal recall as well as for recognition judgments. The relations between confidence, response latency and accuracy demonstrated in these studies in recall of episodic memories, are consistent with findings from a body of research on recognition of verbal information ( Koriat and Ackerman, 2010 ; Ackerman and Koriat, 2011 ), semantic memory recall ( Smith and Clark, 1993 ) as well as in eyewitness identification studies (e.g., Brewer et al., 2006 ; Weidemann and Kahana, 2016 ; for a review, see Brewer and Weber, 2008 ).

Effort Cues as Accuracy Predictors

Given the evidence that memory accuracy is related to retrieval ease as measured by response latency, other cues of the ease with which a memory is retrieved should also predict accuracy. Lindholm et al. (2018) recently provided support for this notion. In two studies, participants were interviewed about their memory of a simulated crime event. In transcripts of these interviews, measures of effort were obtained by identifying a number of cues indicating retrieval difficulty. These effort cues included delays (pauses between or within statements), hedges , that is, commitment avoidance (e.g., “I think,” “maybe”), as well as word fillers (e.g., “well”) and non-word fillers (i.e., expressions without clear meaning, e.g., “uhm”). To control for the fact that a witness report typically includes both accurate and inaccurate information, effort and accuracy were estimated for witnesses’ statements about individual details from the target event, rather than the overall testimony (see also Ball and O’Callaghan, 2001 ). The results showed that effort cues were strongly related to honest witnesses’ memory accuracy, and that several of these cues contributed uniquely in predicting accuracy. While witness confidence was found to be positively related to accuracy, confidence did not contribute with any unique variance in predicting accuracy when the effort cues were included. Moreover, the effort cues fully mediated the relationship between confidence and accuracy, supporting the notion in cue-utilization theory that confidence is based on cues during memory retrieval, rather than a direct monitoring of memory strength ( Koriat, 1997 , 2006 ).

The finding of new, objectively verifiable cues that may be linked to eyewitness accuracy constitutes an important first step for developing methods to improve evaluations of eyewitness memory. However, before initiating attempts at methodological development, it is essential to further test the replicability of these initial findings. Moreover, while this first study examined temporal aspects of witnesses’ responses, this was not measured as the exact latency before a response as in previous studies, but rather in terms of a courser measure of delays before and during a response, unspecified with regard to length. It seems possible that the exact latency (a continuous measure) before initiation of a response is a more fine-tuned and better predictor of memory accuracy than a courser delay (discrete) measure, and that such a latency measure may even make other effort cues redundant. On the other hand, while response latency gives the exact timing before response initiation, pauses and hesitations during the response are not included in this measure. As memory retrieval is rarely instantaneous, but often unfolds as the memory is reported ( Clark and Tree, 2002 ; Warren, 2012 ), delays during a response could also be critical cues to retrieval effort, and carry information about memories correctness. Thus, the role of response latency vs. other effort cues for determining eyewitness accuracy is an issue that warrants further clarification.

The Current Study

The aim of the current study is to test the robustness of the Lindholm et al. (2018) findings, by a replication and extension of their research. Based on their results, it is hypothesized that retrieval effort cues (i.e., hedges, delays, and fillers) as well as confidence will predict memory accuracy. We further expect that confidence will not provide unique variance in predicting accuracy once the effort cues are accounted for. Extending the previous findings, the current study also measures the effort cue response latency and explores the contribution of this factor relative to the other effort cues in predicting accuracy. As the theoretical assumption from cue-utilization theory is that confidence is based on cues rather than derived from memory accuracy directly, we examined whether effort cues mediated the relationship between confidence and accuracy.

Materials and Methods

Participants.

Twenty-two psychology students (15 female; mean age = 24.50 years, SD = 4.97) with normal or corrected-to-normal vision took part in the study in exchange for a movie voucher. Participants were informed that they were to see a simulated crime event on video, and that they would later be videotaped while being asked questions about the event. They all gave informed consent to participate.

Materials and Procedure

The materials and procedures were identical to those carried out by Lindholm et al. (2018) . Participants were tested individually in the lab, where they watched a 1-min film sequence involving a staged crime on a computer monitor. The film initially shows a man waiting at a bus stop. Shortly thereafter, a second man approaches the first man, attacks and stabs him in the gut, before leaving. After seeing the film, participants were interviewed about their memory of the event. The interviews included a free recall phase, immediately followed by a cued recall task with open questions (e.g., “how was the first man dressed?”). As the witness reported his/her memory, the interviewer wrote down the answers (e.g., “the offender had a green hat”) on a numbered sheet. Since the details reported by the witness were noted during an ongoing interview, it was not possible for the interviewer to catch every detail. Following the interview, the experimenter read out the details the witness had reported, and after each one, the witness wrote down his/her confidence in the accuracy of the statement, ranging from 0 to 100%, on a sheet with numbers corresponding to that of the experimenter. We asked for confidence after the interview had finished to allow witnesses to make a focused memory search without being interrupted repeatedly. This also allowed us to better mimic a free-recall situation similar to that typical of eyewitness testimony. As we were interested specifically in cues to accuracy in memories of individual details, rather than in overall accuracy, witnesses did not provide overall confidence estimates, neither in free nor cued recall.

The videotaped interviews were then transcribed verbatim (including fillers like “uhm,” “uh,” and self-talk). Based on the information in the crime video, we first cataloged all scorable and objectively verifiable details. An example of such verifiable detail is “He wore sneakers” whereas “He was cold” is a detail that could not be verified objectively. Based on this catalog, participants’ responses were then coded for accuracy by two independent raters (interrater reliability r = 0.75). Responses to the cued recall questions were then inspected, and two new independent coders selected all statements that provided either accurate or inaccurate information about a verifiable detail in response to a question (interrater reliability r = 0.95). Statements including partly correct and partly incorrect information (e.g., “he was wearing a white [incorrect] jacket [correct]”) were excluded.

Given that questions in the cued recall phase sometimes asked for a detail the participant had mentioned during free recall, we focused on responses during cued recall to avoid associating the same confidence score to two different reports of the same information. This yielded a total of 790 correct answers and 253 incorrect statements. Of these, confidence was obtained for 275 correct and 103 incorrect statements. To make our results section less convoluted, we focus our analyses only on statements for which confidence ratings were made. Next, two new blind coders coded the frequency of verbal and paraverbal expressions of effort in in each statement. Both coders coded the entire set of statements, and inconsistencies were resolved by a third coder. For these effort codings, we calculated the agreement between coders both with Cohen’s kappa (κ), as well as the percentage of exact overlap, that is, the degree to which codings of the cues by one coder corresponded with regard to both cue type and exact cue position in each testimony coded by the other coder. Using the operationalizations by Lindholm et al. (2018) (see Table 1 ), the following effort cues were coded: (1) Non-word Fillers – interjections and sounds like “hm,” “uh,” etc. (interrater reliability Cohen’s κ = 0.97, exact overlap = 91%); (2) Word Fillers – e.g., “meaningless” words like “you know,” “well,” etc. This category also included self-talk such as “Let’s see...” (interrater reliability Cohen’s κ = 0.83, exact overlap = 65%); (3) Hedges – word forms that reduce the force of an assertion, allow for exceptions, or avoid commitment, such as “I think” and “maybe” (interrater reliability Cohen’s κ = 0.87, exact overlap = 62%). We also measured Delays – a pause longer than 2 s before or during a response. Finally, we measured a fifth effort cue, Response latency (see Table 1 ). Both response latency and delays were measured using the video editing software iMovie (version 10.1.10, Apple Inc., 2018 ). The interviews of the participants were loaded into the program, and elapsed time was obtained by computing the temporal distance of silences between utterances as indicated by sound wave intensity. Hence for these cues, interrater reliability was not measured.

Table 1. Operationalizations of the effort cues in the witnesses’ responses.

Predicting Accuracy With Effort Cues and Confidence

Mean amounts of effort cues and confidence ( z- transformed) in accurate and inaccurate statements for each variable are presented in Figure 1 .

Figure 1. Mean amount of retrieval effort cues and confidence ( z- transformed) in correct and incorrect memories. Error bars represent 95% confidence intervals.

As the design used repeated measures (all participants provided both correct and incorrect responses), in combination with a varying number of responses produced by different participants, data were therefore organized as a multilevel data set with individual responses nested within participants ( Wright and London, 2009 ). The calculations were computed with R ( R Core Team, 2018 ), using the lme4 package ( Bates et al., 2015 ).

Our analyses largely followed the procedure outlined in Field (2009) and Mansour et al. (2017) . Hence, we first ran a set of regressions to examine which individual variables predicted accuracy. Thus, a baseline, intercept-only model predicting accuracy (Model 1) was compared with models including each effort cue and confidence separately (Models 2–7). Table 2 illustrates the model parameter estimates and fit indices. In this table, effect sizes are given as Akaike Weights. The Akaike Weights varies between 0 and 1 and estimate the probability that the chosen model is the best-fitting model, relative to the other model(s) ( Burnham and Anderson, 2004 ; Wagenmakers and Farrell, 2004 ). Hence, larger values indicate better fit. The results showed that model fit was significantly improved compared to the baseline model when adding Delays , χ 2 (1) = 22.37, p < 0.001, w i (AIC) = 0.99; Word Fillers ,χ 2 (1) = 3.88, p = 0.048, w i (AIC) = 0.72; Hedges , χ 2 (1) = 26.30, p < 0.001, w i (AIC) = 0.99; and Confidence , χ 2 (1) = 27.95, p < 0.001, w i (AIC) = 0.99, but not by adding Non-word Fillers ,χ 2 (1) = 2.94, p = 0.088, w i (AIC) = 0.61. In addition, Response latency , χ 2 (1) = 8.93, p = 0.003, w i (AIC) = 0.97, improved fit compared to the baseline model.

Table 2. Parameter estimates for predictors in models of accuracy (478 observations).

We next examined whether a model including all the significant variables from the first set of regressions improved fit relative to each of the separate models with significant predictors. Because delays and response latency were both significant, but partly based on the same data (a 2-s pause before the beginning of a statement would be coded both as latency and as a delay), we first needed to determine which of the two would be optimal in a model including all significant variables (we also checked for multicollinearity between all cues, and only response latency and delays were at risk, see Supplementary Table 1 ). Hence, we ran a model including Hedges, Delays, Word Fillers and Confidence (Model 8), and a model in which Delays were swapped for Response latency (Model 9), and compared the two models’ fit to data (see Table 2 for parameter estimates and fit indices). To assess which model had the best fit, we compared Akaike Weights for each model. The results showed that Model 8 including Delays [w i (AIC) = 0.93] had a better fit, compared to Model 9 with Response latency [w i (AIC) = 0.06, see Table 2 ]. In the subsequent analysis, therefore, we used the model with Hedges, Delays, Word Fillers, and Confidence and compared it to the models with each significant predictor.

Results showed that our model with multiple predictors significantly improved fit compared to the models with only Hedges, χ 2 (3) = 20.52, p < 0.001, w i (AIC) = 0.99; Delays, χ 2 (3) = 24.45, p < 0.001, w i (AIC) = 0.99; Word Fillers, χ 2 (3) = 42.95, p < 0.001, w i (AIC) = 0.99; and Confidence, χ 2 (3) = 18.88, p < 0.001, w i (AIC) = 0.99. The best-fitting model thus contained Hedges, Delays, Word Fillers, and Confidence. In this model, Delays ( z = 2.97, p = 0.003) and Hedges ( z = 2.23, p = 0.026) decreased as accuracy increased, proving unique predictors of memory accuracy, whereas Word Fillers ( z = 0.60, p = 0.548) did not (see Table 3 ). Moreover, and contrary to expectations, Confidence contributed uniquely in explaining memory accuracy when controlling for the other predictors ( z = 2.72, p = 0.007), increasing with increased accuracy.

Table 3. Multilevel logistic regression analysis predicting response accuracy from effort cues and confidence ( z -transformed).

Effort Cues as a Basis for Confidence

In the final analysis, we examined the role of effort cues as mediators of the relationship between accuracy and confidence. For this analysis, we created an effort index by summarizing hedges and delays, the two effort cues that uniquely predicted accuracy. The mediational analysis was run using the mediation ( Tingley et al., 2014 ) package. Results showed that the effort cues partially mediated 57.3% of the relation between accuracy and confidence (see Figure 2 ).

Figure 2. Effort index as a mediator of the relationship between accuracy and confidence. Values represent unstandardized parameter estimates for each path. Along the path from accuracy to confidence the numbers in parentheses represent the coefficients when the effort index was entered into the analyses. Dashed line indicates that the direct path is significantly mediated by the indirect path. ∗∗ p < 0.01, ∗∗∗ p < 0.001.

The datasets analyzed for this study, and the code for the analyses, have been deposited in the Open Science Framework. Link to datasets: https://osf.io/uthbz/?view_only=1284f5b56d6d4af58679c74d913351fc . Link to code for analyses: https://osf.io/8kjnv/?view_only=baadf99fa8f7446e989f04d9a5e344bf .

The aim of this study was to further explore previously demonstrated relations between eyewitness accuracy and cues to retrieval effort ( Lindholm et al., 2018 ). Our results largely replicate previous results, providing additional support for the use of effort cues in estimating eyewitness accuracy. Looking at the relationship between accuracy, effort cues and confidence, we found that effort cues partially mediated the relationship between confidence and accuracy ( Figure 2 ). This study also measured the effort cue response latency, and found, in line with previous studies ( Brewer et al., 2006 ; Koriat and Ackerman, 2010 ; Ackerman and Koriat, 2011 ; Weidemann and Kahana, 2016 ), that correct responses were faster than incorrect responses. However, a coarser, but more inclusive temporal measure of delays (pauses before and during a response) was a better predictor of accuracy than response latency.

Out of the five effort cues examined in this study, four (hedges, delays, word fillers, and response latency) were significantly related to memory accuracy, but non-word fillers was not. Thus, our results largely mirror our hypotheses, as well as the results obtained by Lindholm et al. (2018) . These results pointed in the same direction for all the cues, as correct statements contained fewer cues to retrieval effort compared to incorrect statements (see Figure 1 ). Furthermore, in the current study, hedges and delays proved to be unique predictors of accuracy. These results also concur with those of Lindholm et al. (2018) , in that both delays and hedges uniquely predicted accuracy.

Previous research has demonstrated that response latency is reliably related to memory accuracy ( Brewer et al., 2006 ; Koriat and Ackerman, 2010 ; Ackerman and Koriat, 2011 ; Weidemann and Kahana, 2016 ), and in the current study (in line with previous findings), correct responses were initiated faster than incorrect ones. However, including latency in the model did not make other effort cues redundant in predicting memory. Moreover, when comparing a model including response latency with a model including the coarser, but more inclusive measure of delays, the latter was found to explain more variance in accuracy than exact response latency. A plausible interpretation of this finding is that when memory retrieval unfolds as the memory is reported ( Clark and Tree, 2002 ; Warren, 2012 ), delays during the response carry further information of retrieval effort and memory accuracy than that captured by the initial response latency. This result clearly calls for a reconsideration and broadening of how the temporal aspect of memory retrieval should be measured in future studies on cues related to memory accuracy.

As noted in the introduction, research suggests that people generally find it difficult to judge the accuracy of others’ memories ( Lindholm, 2005 , 2008a , b ). An obvious practical question following from our findings is therefore whether practitioners, police officers and jurors in legal investigations, could be trained to use effort cues to better discriminate between honest witnesses’ accurate and inaccurate memories. While assessing memory accuracy based on signs of retrieval effort in an ongoing interview might prove difficult, the cues found to predict memory in our study should be fairly easy to learn to use when assessing accuracy from transcribed testimonies. Hence, a first step to test the practical value of the current findings would be to give evaluators instructions on cues related to accuracy, and then examine their performance in using these cues when assessing the accuracy of transcribed testimonies. While previous attempts modestly support the idea that instructions may improve accuracy of judgments ( Koriat and Ackerman, 2010 ), research on the benefits of such training is scarce.

In the study by Lindholm et al. (2018) , confidence did not contribute uniquely to variation in memory accuracy when controlling for effort cues. While we expected to replicate this finding, our study showed that confidence does indeed predict accuracy and also when effort cues were controlled for. Moreover, while the previous study demonstrated that effort cues fully mediated the relationship between accuracy and confidence, our results suggest partial mediation. Thus, although confidence in a memory may be partly based on cues to retrieval effort, our results suggest that there are other sources on which people base their confidence. In line with research findings within the framework of cue-utilization theory, candidates for these sources are likely found in the theory-based realm of cues, that is, in people’s beliefs and knowledge about memory (e.g., Matvey et al., 2001 ; Nussinson and Koriat, 2008 ). Moreover, it is reasonable to assume that retrieval effort is evident not only in the verbal and paraverbal cues studied here, but also in body language and facial mimicry (e.g., Krahmer and Swerts, 2005 ). Future studies should further scrutinize and include these potential alternative bases of confidence judgments and accuracy cues.

Despite replicating the main findings of Lindholm et al. (2018) , there were also some differences between these studies. First, there is a slight variation between the studies regarding which specific cues contributed uniquely in predicting accuracy. For example, whereas non-word fillers in the Lindholm et al. (2018) study predicted accuracy, this cue was not significantly related to accuracy in our study. A straightforward explanation for this discrepancy is that effort cues vary in how reliably they are associated with memory. However, it could also be that the pattern of associations between cues and accuracy would become more stable with larger sample sizes.

Limitations

While the interviews in our study were designed to simulate real eyewitness interviews, there are important limitations that restrict the generalizability of the findings to real world settings. First, we interviewed witnesses directly after they had viewed the crime event, meaning that the retention interval was negligible in comparisons to typical retentions between witnessing and reporting a target event in real-life eyewitness situations. Previous studies have demonstrated that factors that affect the discriminability of correct and incorrect memories, such as retention interval, may also change the relationship between response latency and accuracy ( Brewer et al., 2006 ). Hence, an important issue for future studies is to examine how factors that affect discriminability (e.g., retention interval, task difficulty) may influence the validity of retrieval effort cues. Moreover, although our use of multi-level statistical analyses optimize power by taking advantage of the variability within individual witness responses, our sample of witnesses was admittedly small. Hence, our findings should ideally be replicated with larger samples. At the same time, the fact that research on semantic memory show effort/accuracy/confidence relationships with similar markers of effort ( Smith and Clark, 1993 ) provides strong support for the validity of the current findings.

An important feature of this study was that measures of experienced effort were obtained during a natural, free-recall situation similar to that typical of eyewitness interviews. This meant that we asked them for confidence only after their recall of the whole event. While our procedure allowed witnesses to search their memory without being interrupted, this method may have had implications for their confidence ratings. For example, Robinson and Johnson (1996) showed that the confidence-accuracy relationship is stronger when estimating confidence after recalling an entire event, compared to immediately after each detail. Given that we replicate earlier findings of a positive confidence-accuracy relationship, it seems reasonable that our methodology did not bias the findings in any critical way. However, future studies should examine how procedural variations may affect the relations between confidence, accuracy, and effort cues.

Further, because the interviewer wrote down details reported by the witness during the ongoing interview, it was not possible for the interviewer to catch every single detail. This meant that confidence judgments could not be obtained for all statements. As we wanted to examine both effort cues and confidence in relation to memory accuracy, we decided to utilize the data for which confidence was also obtained. Thus, our analyses were carried out on a smaller dataset, not containing all statements provided by the witnesses. However, since the ratio of correct and incorrect statements were roughly the same for memories overall, and for memories with confidence estimates, we assume that the sample with confidence ratings is representative of the statements overall. For the interested reader, we have added analyses with the full dataset, excluding confidence in Supplementary Table 2 .

In addition, while the instructions for coding of the effort cues were thoroughly pre-tested to be clear and unambiguous, the relatively low inter-rater reliability for some of the cues suggests that these instructions could be improved.

Finally, in this study our analyses focused on responses in the cued recall phase, which restricts our findings to this type of retrieval setting. Assuming that free recall memory primarily includes details that witnesses remember well, and hence retrieve fairly easy, it seems possible that effort cues might be less useful for discriminating accurate vs. inaccurate statements in this type of retrieval settings. This is one issue of obvious relevance for future research.

Taken together, this study lends new support to the notion that retrieval effort in eyewitness responses is central for discriminating accurate from inaccurate recall of event details. Moreover, our findings suggest that a coarser, but more inclusive measure of delays before and during a response explains more variance in accuracy than response latency.

We show that effort cues partly mediate the relationship between accuracy and confidence, supporting the hypothesis that aspects of confidence are based on implicit, inferential processes. These findings suggest promising new ways of improving judgments of eyewitness evidence.

Data Availability

All datasets generated for this study are included in the manuscript and/or the Supplementary Files .

Ethics Statement

The study was conducted in full in accordance with the ethical principles outlined on http://www.codex.vr.se/ , and with the 1964 Helsinki declaration and its later amendments. The studies did not include factors that require ethical vetting according to Swedish legislation on research ethics, http://www.epn.se/en/start/regulations/ .

Author Contributions

TL initiated, designed, and conducted data collection for the study. PG analyzed the data and wrote the manuscript in collaboration with TL and FJ. All authors contributed to interpretation of analyses and approved the final version of the manuscript.

This research was supported by a grant from the Magnus Bergvall Foundation (Grant No. 2018-02708).

Conflict of Interest Statement

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

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2019.00703/full#supplementary-material

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Keywords : eyewitness accuracy, eyewitness testimony, confidence-accuracy relation, response latency, retrieval effort cues

Citation: Gustafsson PU, Lindholm T and Jönsson FU (2019) Predicting Accuracy in Eyewitness Testimonies With Memory Retrieval Effort and Confidence. Front. Psychol. 10:703. doi: 10.3389/fpsyg.2019.00703

Received: 13 November 2018; Accepted: 13 March 2019; Published: 29 March 2019.

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Copyright © 2019 Gustafsson, Lindholm and Jönsson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Philip U. Gustafsson, [email protected]

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

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Open Access

Peer-reviewed

Research Article

Forgetting, Reminding, and Remembering: The Retrieval of Lost Spatial Memory

Contributed equally to this work with: Livia de Hoz, Stephen J Martin

* To whom correspondence should be addressed. E-mail: [email protected]

Affiliation Laboratory for Cognitive Neuroscience, Centre and Division of Neuroscience, University of Edinburgh, Edinburgh, Scotland, United Kingdom

• Livia de Hoz, 
• Stephen J Martin, 
• Richard G. M Morris

• Published: August 17, 2004
• https://doi.org/10.1371/journal.pbio.0020225
• Reader Comments

Retrograde amnesia can occur after brain damage because this disrupts sites of storage, interrupts memory consolidation, or interferes with memory retrieval. While the retrieval failure account has been considered in several animal studies, recent work has focused mainly on memory consolidation, and the neural mechanisms responsible for reactivating memory from stored traces remain poorly understood. We now describe a new retrieval phenomenon in which rats' memory for a spatial location in a watermaze was first weakened by partial lesions of the hippocampus to a level at which it could not be detected. The animals were then reminded by the provision of incomplete and potentially misleading information—an escape platform in a novel location. Paradoxically, both incorrect and correct place information reactivated dormant memory traces equally, such that the previously trained spatial memory was now expressed. It was also established that the reminding procedure could not itself generate new learning in either the original environment, or in a new training situation. The key finding is the development of a protocol that definitively distinguishes reminding from new place learning and thereby reveals that a failure of memory during watermaze testing can arise, at least in part, from a disruption of memory retrieval.

Citation: Hoz Ld, Martin SJ, Morris RGM (2004) Forgetting, Reminding, and Remembering: The Retrieval of Lost Spatial Memory. PLoS Biol 2(8): e225. https://doi.org/10.1371/journal.pbio.0020225

Academic Editor: Howard B. Eichenbaum, Boston University

Received: March 3, 2004; Accepted: May 14, 2004; Published: August 17, 2004

Copyright: © 2004 de Hoz et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Competing interests: The authors have declared that no conflicts of interest exist.

Abbreviations: ANOVA, analysis of variance; PT, probe trial; PT n , probe trial in a novel environment; RA, retrograde amnesia

Introduction

“Two suppositions are equally warranted, viz., that either the registration of the prior states has been effaced; or that the retention of the anterior states persisting, their aptitude for being revived by associations with the present is destroyed. We are not in a position to decide between these two hypotheses.”

Studies of RA have favoured a memory-consolidation interpretation in instances in which systematic variation of the time interval between experience or training and the subsequent brain insult has revealed a temporal gradation of RA ( Squire 1992 ). Computational models also point to the need for a rapid encoding and storage system, together with a slower interleaving mechanism that is thought to underlie systems-level consolidation and long-term storage in the cortex (e.g., McClelland et al. 1995 ). However, the existence of some amnesic patients with long, flat gradients of RA extending for years or decades into periods of their life when memory function was normal provided some of the first evidence that RA might be due to retrieval failure ( Sanders and Warrington 1971 ). This perspective on RA was initially supported by studies indicating that, in the anterograde domain, impaired memory could be alleviated by partial cues ( Warrington and Weiskrantz 1968 ). However, these observations were later construed as reflecting the operation of a separate memory phenomenon called priming ( Graf et al. 1984 ). Several animal studies have also indicated that a variety of ‘reminder' treatments delivered prior to retention testing can realize the expression of lost memories ( Gold et al. 1973 ; Miller and Springer 1973 ; Spear 1973 ; Gold and King 1974 ; Riccio and Richardson 1984 ; Sara 1999 ), but it is not easy to distinguish priming-induced memory from explicit recall and recognition in animal studies. Experimental resolution of the consolidation-versus-retrieval controversy has been notoriously difficult, and no consensus has been achieved. A key methodological issue, and the focus of the new technique described here, concerns the need to demonstrate that the memory observed after a reminder treatment results from the reactivation of an existing memory ( Miller and Springer 1972 ), rather than a facilitation of new learning ( Gold et al. 1973 ).

In studies of spatial memory using the watermaze, amnesia for the location of the escape platform in posttraining probe trials (PTs) has generally been interpreted as a failure of learning, consolidation, or storage ( D'Hooge and De Deyn 2001 ). To investigate the alternative possibility of retrieval failure, we deliberately created conditions that should maximize the possibility of seeing such an effect. This involved training rats to find an escape platform in a specific location followed by partial lesioning of the hippocampus. We reasoned that this would weaken but not completely disrupt the memory of the correct location by damaging a subset of the ensemble of stored traces. The animals' memory was tested and observed to be undetectable. This same memory test provided, however, the opportunity to remind animals that escape from the water was possible via an escape platform in the correct or incorrect location. One hour later, the animals' memory was tested again. We observed that memory was now detectably above chance and was equally strong when the animals had previously been given correct or potentially misleading information about the current location of the platform. Additional control procedures, and the performance of other groups with sham or complete hippocampal lesions, established that the earlier failure of memory must have been due, at least in part, to retrieval failure.

A summary of the experimental design is provided in Figure 1 (see Materials and Methods ).

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Outline of the different phases of testing. The platform position used during training is indicated by a red circle in the NE quadrant of the pool (large blue circle), although in practice platform locations were counterbalanced between NE and SW locations. The novel location, to which a subset of rats was exposed during reminding, is indicated by a black circle in the SW quadrant. This position was always opposite to that used during training. PT1 and PT2: probe test 1 and 2. The hatched areas represent the original training quadrant irrespective of the position of the platform (i.e., original or novel) during retention testing. PT n 1 and PT n 2: PTs during new context learning in the second pool.

https://doi.org/10.1371/journal.pbio.0020225.g001

Training Prior to the Lesions

During cued pretraining, the rats quickly learned to search for, and climb onto, the visually cued escape platform. In the main spatial training phase, the animals rapidly learned to locate and raise the platform in order to escape from the pool ( Figure 2 ), as indicated by the highly significant reduction in latencies over trials ( F [7.78, 412] = 30.4, p < 0.001). Only animals that reached the acquisition criterion received lesions (69 out of 73 rats trained). The prospective lesion groups, trained as normal animals, did not differ ( F < 1, n = 59; see Surgery below).

Mean latencies to escape from the water and climb onto the hidden platform during task acquisition. Data are averaged in blocks of five trials and grouped according to the lesion made at the end of training; note that all animals were unoperated during acquisition. Only rats that reached criterion (mean escape latency less than 15 s over the last ten trials) and whose lesions were considered acceptable (see Results: Surgery) are presented. Animals rapidly learned to locate the escape platform, and prospective lesion groups did not differ.

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Of the 69 animals that received lesions, one died after surgery and nine were excluded based on strict histological criteria, leaving a total of 59 animals (22 sham lesions, 19 complete hippocampal lesions, and 18 partial hippocampal lesions; see Figure 3 ).

Representative photomicrographs of cresyl-violet-stained coronal brain sections taken from subjects belonging to each of the three lesion groups—partial hippocampal lesion (A), sham lesion (B), and complete hippocampal lesion (C). In each case, sections corresponding to anterior, middle, and posterior levels of the hippocampus are displayed. The mean area of spared hippocampal tissue in each group (see Materials and Methods for calculation) is plotted below in (D). Note that the volumes of spared tissue in the septal and temporal halves of the hippocampus are plotted separately, but these values are still expressed as percentages of the entire hippocampal volume—hence the value of 50% per half in shams. The cartoon hippocampi accompanying the graph indicate lesioned tissue in dark grey, and spared tissue in light cream. As intended, partially lesioned rats exhibited substantial sparing only in the septal (dorsal) half of the hippocampus, and rats with complete hippocampal lesions exhibited minimal sparing (less than 5% at either pole).

https://doi.org/10.1371/journal.pbio.0020225.g003

Retention Testing

The key new findings are shown in Figures 4 and 5 using two separate but related measures of memory retrieval: percentage time in quadrant ( Figure 4 ) and a more sensitive measure, percentage time in a zone centred on the platform location ( Figure 5 ; see Materials and Methods ). An overall analysis of variance (ANOVA) of percentage time in the training (where the platform was located during training) and the opposite quadrants of the pool revealed a significant quadruple interaction ( F [2, 53] = 7.66, p < 0.01) involving two between-subject factors: lesion group and platform location during the reminder treatment (original versus novel), and two within-subject factors: PT (PT1 and PT2) and quadrant (training versus opposite). In both figures, the initial memory expressed during PT1 is shown in the left lane. This reveals that the partially lesioned rats were at chance, whereas the sham-lesioned rats could remember the location of the platform ( t = 6.15, df = 21, p < 0.005, paired-sample t-test, training versus opposite quadrant). The complete-lesioned animals were at chance. Analysis of percentage time in zone ( Figure 5 ) likewise confirmed that memory was detectable in the sham lesion group ( t = 4.18, df = 21, p < 0.005, one-sample t-test, comparison with chance = 50%), but not in the two lesion groups.

Percentage time during PT1 and PT2 spent in the training and opposite quadrants of the pool (left and right lanes) and the reminder treatment (grey central lane). The training location is represented as a red circle in the NE quadrant, and the novel location (novel subgroups only) as a black circle in the SW quadrant. In practice, NE and SW quadrants were counterbalanced. Rats with partial hippocampal lesions were unable to remember the platform location on PT1 but could be reminded of the training location by exposure, at the end of PT1, to a platform in the original or a novel location. (Note that the ‘reminder' lane simply refers to this exposure to a platform—PT1 is itself the ‘reminder trial.') The key finding is that the improvement in PT2 occurred irrespective of the platform location during reminding. In contrast, sham-lesioned animals exhibited some reversal learning upon exposure to the platform in a novel location. Complete-lesioned rats did not remember the platform location during either PT1 or PT2. * p < 0.05; ** p < 0.01; n.s. = nonsignificant; comparisons with chance = 50%; one-sample t-tests. Representative swim paths are included.

https://doi.org/10.1371/journal.pbio.0020225.g004

Percentage time in PT1 (left) and PT2 (right) spent within a zone, 20 cm in radius, centred on either the original training location (broken circle; grey) or an equivalent location in the opposite quadrant (broken circle; yellow), expressed as a percentage of the total time spent in both of the zones. The reminder treatment is again shown as the grey central lane and as the location where the hidden platform became available at the end of PT1 within these zones (original = red; novel = black). Consistent with Figure 4 , rats with partial hippocampal lesions were amnesic in PT1 but could be reminded of the correct location, even by exposure to the platform in a novel location. * p < 0.05; ** p < 0.01; n.s. = nonsignificant; comparisons with chance = 50%; one-sample t-tests.

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PT1 ended with the animals finding the platform in the original training location, or in a novel location in the ‘opposite' quadrant of the pool (middle lane in Figures 4 and 5 ; see Materials and Methods for explanation of terminology). These different events at the end of the swim trial potentially served both as a reminder of what happens in a watermaze, namely, escape from the water at a particular location, and/or as an opportunity for new learning. We reasoned that if the reward of escaping from the water served only to support new learning, animals capable of learning would show an enhanced bias towards the training location after finding the platform in the original location, but a reduced bias after finding it in the opposite novel location. Conversely, if these events served only as reminder cues, they might be equally effective in reminding the rats of the original training location.

The key new finding is that the partial lesion group displayed a bias for the training quadrant that was equivalent whether the animals had found the platform in the original training location or in the novel opposite location, at the end of PT1. The overall ANOVA of the PT2 quadrant data revealed a triple interaction of lesion group × quadrant (training versus opposite) × platform location during reminding (novel versus original) ( F [2, 53] = 19.28, p < 0.001). With respect to the performance of the partial lesion group alone on this quadrant measure (see Figure 4 , right lane), there was a significant improvement between PT1 and PT2 ( F [1, 16] = 7.98, p < 0.02) and no difference between novel and original reminding locations ( F < 1). The partial lesion group also showed a highly significant preference for the training quadrant versus the opposite quadrant on PT2 ( F [1, 16] = 16.83, p < 0.001). The same pattern of results is apparent in the zone data (see Figure 5 ) where, overall, the partial lesion group displayed a significant improvement between PT1 and PT2 ( F [1, 16] = 7.64, p < 0.02) that also did not differ between ‘novel' and ‘original' groups ( F < 1). Because a bias for the training location appeared even in the animals that were exposed to a novel platform position, memory on PT2 cannot be attributed to relearning of the platform location.

In contrast, sham-lesioned animals behaved quite differently in PT2 as a function of whether the platform was presented in the original or the novel location during the reminder treatment. Performance showed a further bias towards the training location between PT1 and PT2 following the event of climbing onto the escape platform in its original location, but exposure to the novel location resulted in a reduction in time spent in the training zone—a partial reversal. Supported by significant interactions in the overall ANOVA, analysis of time spent in the training quadrant revealed that, as expected, sham-lesioned animals reexposed to the original location increased their time there between PT1 and PT2 ( F [1, 11] = 12.41, p < 0.005). Conversely, sham-lesioned animals exposed to the novel location exhibited modest reversal learning, increasing their time in the opposite quadrant ( F [1, 9] = 9.35, p < 0.02). The same pattern of results was obtained from the analysis of time in the training zone ( Figure 5 ), for which a significant interaction between PT (PT1 or PT2) and platform location during reminding (original versus novel) was observed ( F [1, 20] = 5.46, p < 0.05).

Complete-lesioned rats performed at chance during all PTs (see Figures 4 and 5 , left and right lanes). That is, their behaviour during the retention tests before and after the reminder treatment showed no impact of that treatment.

Novel Context Learning

As an independent test of whether the reminder treatment of escape onto a platform could support new learning, all animals were taken to a second (‘downstairs') watermaze and given two PTs ( Figure 6 ). This was a novel environment, and, therefore, there was no reason to expect the animals to perform at better than chance levels in the first of these PTs in a novel environment (PT n 1). However, escape from the water at the end of this PT might be sufficient to support new one-trial learning. Such learning was absent in the partial hippocampal lesion group ( F < 1). The sham lesion group, in contrast, did learn ( F [1, 21] = 4.51, p < 0.05), performing significantly better than the lesioned groups on PT n 2 (post hoc Ryan–Einot–Gabriel–Welsch range test, p < 0.005). The complete lesion group again showed no evidence of learning in a new environment ( F < 1).

Percentage time spent in the target quadrant containing the escape platform during one-trial new learning in a different pool. * p < 0.05; n.s. = nonsignificant; comparison of percentage time spent in training zone during PT n 1 and PT n 2; paired-sample t-tests. New learning was observed only in sham-lesioned rats.

https://doi.org/10.1371/journal.pbio.0020225.g006

The key finding of this study is that rats with partial lesions of the hippocampus can be reminded of a preoperatively learned escape location in a watermaze by both correct and potentially misleading information. Whereas sham-lesioned rats showed new one-trial learning towards or away from the originally trained quadrant as a function of the type of reminder treatment to which they were exposed, partially lesioned animals were unable to learn. Instead, the first PT served only as a reminder of the original platform location irrespective of where in the pool the platform was raised at the end of this trial. Rats with complete hippocampal lesions showed neither new learning nor reminding.

There is an extensive classic literature on the nature and effectiveness of reminder treatments ( Riccio and Richardson 1984 ). Exposure to the training context, noncontingent stimuli, or additional training trials are just some examples of methods successfully used to remind animals of a prior training experience ( Zinkin and Miller 1967 ; Miller and Springer 1973 ; Mactutus et al. 1979 ; Gisquet Verrier and Schenk 1994 ; Przybyslawski and Sara 1997 ). Controversy did, however, surround studies that interpreted memory following a reminder treatment as evidence that the original amnesia was the result of a retrieval deficit ( Zinkin and Miller 1967 ; Miller and Springer 1973 ). It was argued that a reminding trial simply strengthens a weak memory that is behaviourally unobservable, similar to what happens during initial learning ( Cherkin 1972 ; Gold et al. 1973 ; Haycock et al. 1973 ; Gold and King 1974 ), or that, when amnesia is complete, it results in one-trial learning or response generalization. However, manipulations that are unlikely to produce new learning can also serve as effective reminders. Examples include pharmacological manipulations of the internal state ( Mactutus et al. 1980 ; Concannon and Carr 1982 ) and reexposure to the amnestic agent prior to retention testing ( Thompson and Neely 1970 ; Hinderliter et al. 1975 ). In many such studies, however, the use of inhibitory avoidance as a memory test makes it difficult to determine the cognitive ‘content' (cf. Riccio and Richardson 1984 ) of the behaviour expressed during retention testing. Although memory reactivation may have occurred when a rat inhibits movement that previously led to electric shock, an alternative interpretation is that a generalized fear state has been induced. The issue of whether and when amnesia reflects a storage or retrieval deficit was, thus, left unresolved.

Two features are distinctive about our study. First, unlike in many previous studies, the reactivated memory involves the recall and expression of highly specific information—a discriminable position in space, and not just a faster escape latency, or greater freezing. Second, despite exposure to a novel platform location leading to reversal learning in the sham lesion group, the partial lesion group displayed only reminding of the original platform location. This distinction is important because, with the current revival of interest in memory retrieval, our protocol circumvents the ambiguities involved in the use of relearning as an index of retention. One example of a study that used a reacquisition rather than a true reminding protocol ( Land et al. 2000 ) revealed that a reminder prior to retention testing could alleviate amnesia in animals with hippocampal lesions. However, it is difficult to distinguish between ‘pure' reminding and the facilitation of new learning using reacquisition alone.

Nonetheless, the watermaze task is deceptively complex, and successful performance depends on the operation of several distinct memory systems ( Bannerman et al. 1995 ; Whishaw and Jarrard 1996 ; Warburton and Aggleton 1999 ; Eichenbaum 2000 ; White and McDonald 2002 ). Accordingly, while no new learning of the platform location occurs in the partial and complete lesion groups, some ‘procedural' learning may take place during PT1; this may enhance a weak, subthreshold spatial memory to a point at which it can be expressed in PT2. However, for this argument to be plausible, one would expect there to be minimal retention of the procedural components in PT1. This was clearly not the case, as rats with both partial and complete hippocampal lesions did not behave like naïve animals during PT1. They searched at an appropriate distance from the pool walls and readily climbed onto the escape platform when it was eventually made available. Procedural learning is also generally well retained over time and, being slow, unlikely to change much in one trial. We also doubt that the recovery of memory on PT2 reflects the emergence of latent memory mediated solely by an extrahippocampal structure, but not expressed during PT1. For example, rats with complete hippocampal lesions have been shown to learn a spatial conditioned-cued preference mediated by the amygdala ( White and McDonald 1993 ), a form of memory that is partially masked by hippocampus-dependent learning in normal rats ( McDonald and White 1995 ). However, seeing reminding in partial but not complete hippocampus-lesioned animals argues against this possibility in this case. Finally, the recovery of a simple stimulus–response strategy based on approaching single cues is unlikely, as novel start locations were always used during retention testing (cf. Eichenbaum et al. 1990 ; see Materials and Methods ). Under these circumstances, it is reasonable to interpret the apparently complete amnesia observed in PT1 as, at least in part, a failure of spatial memory retrieval.

Our use of partial hippocampal lesioning introduces several other issues. First, it is a technique that is arguably more relevant to human amnesia, in which damage to a structure is typically incomplete. Second, it is also relevant to the many studies in which a pharmacological intervention is applied at a single site within a brain region—microinfusion into the dorsal hippocampus, for instance, is likely to have minimal effects on ventral hippocampal tissue (see Steele and Morris 1999 ). Third, and perhaps most interesting, is the question of where memory traces are located. Given that reminding only occurs in partially lesioned rats, it is reasonable to suppose that spatial memory traces are either located (and reactivated) within the hippocampus, or that the hippocampus is required for the process of reactivation or expression of a reactivated memory stored elsewhere. According to the latter hypothesis, spatial memory traces might be stored in cortex but require fast synaptic transmission in the hippocampus to be retrieved (cf. Teyler and DiScenna 1986 )—at least during the period after training and before the completion of systems-level consolidation. Alternatively, some hippocampal tissue might be required for cortically expressed memory to gain access to striatal motor planning and executive systems. Findings reported by Virley et al. (1999) suggest that this retrieval hypothesis might not be implausible. In this study, monkeys with CA1 pyramidal cell lesions were amnesic for a preoperatively acquired visuospatial discrimination. Subsequent grafting of CA1 pyramidal cells resulted in the recovery of memory for a second preoperatively acquired discrimination. As the grafted tissue cannot contain specific memory traces, the implication is that the recovery of some aspects of CA1 cellular function is sufficient for the information processing mediating the retrieval of memories stored elsewhere.

In raising many more questions than they answer, the present findings open a potential avenue of research into the neural dynamics of memory reactivation and retrieval. Specific interventions such as local AMPA receptor blockade (cf. Riedel et al. 1999 ) might be directed at the hippocampus or cortex during PT1 or PT2. Such a study could provide information about the role of these structures—and their network interactions—in the reactivation of apparently lost memories, and in their subsequent retrieval. For example, hippocampal neural activity may be necessary for effective retrieval, but perhaps not for the reminding-induced reactivation of memory, even for an ostensibly hippocampus-dependent task (cf. Land et al. 2000 ). Similarly, the necessity for hippocampal neural activity during retrieval might vary as a function of time after memory consolidation. In addition, the determinants of the reminder phenomenon itself remain unclear. It would be useful to establish whether reinforcement in the form of an escape platform is, in fact, necessary during PT1, or indeed whether a reminder trial in a separate pool would have been effective. Experiments involving partial versus complete sets of cues might also provide valuable insights into the reminding process (cf. O'Keefe and Conway 1978 ). These and related analyses will be the subject of future studies.

Dissociating the storage and retrieval functions of the hippocampus in memory is central to our understanding of the role of hippocampo–cortical connections. Many theories of hippocampal function are based on the idea that the hippocampus acts as a mediating link between different cortical regions during the interval before systems consolidation is complete ( Teyler and DiScenna 1986 ; Squire and Alvarez 1995 ). Paradoxically, the same features that point to the alternative possibility—that the hippocampal formation is a site of encoding and long-term storage of complex multimodal memories within its distributed intrinsic circuitry ( Moscovitch and Nadel 1998 )—also place this group of structures in an ideal position to help reactivate memories from traces distributed over several cortical structures, perhaps via a mechanism such as pattern completion (see Marr 1971 ; Nakazawa et al. 2002 ). It is possible that, when the hippocampus is partially damaged and the cortico–hippocampal network is therefore degraded, retrieval is only possible once a more complete recreation of the training situation, possibly including reexposure to a platform, is provided. Although comparisons across different species and forms of memory should be viewed with caution, this scenario is reminiscent of Tulving's encoding specificity principle ( Tulving and Pearlstone 1966 ; Thomson and Tulving 1970 ) in that exposure to similar cues during encoding and retrieval phases permits the recovery of the original memory, despite the provision of incorrect information about the target location itself. Paradoxically, the poor learning abilities of partially lesioned rats might explain why a trial ending with exposure to a novel spatial location can serve as a reminder for the original location—by limiting new learning of the new location, a reactivated memory for the old location is unmasked.

Materials and Methods

We used a total of 73 male Lister Hooded rats obtained from a commercial supplier (Charles River Laboratories, United Kingdom). They were pair-housed in plastic cages with sawdust bedding and ad libitum access to food and water. Their care and maintenance and all experimental procedures were carried out in accordance with United Kingdom Home Office Regulations.

Behavioural testing was conducted using two separate circular pools, 2.0 m in diameter and 60 cm high, each located in well-lit rooms with numerous distal visual cues. One pool was used for training and retention (‘upstairs') and the other for new context learning (‘downstairs'). The pools were filled with water at 25 °C ± 1 °C made opaque by the addition of 200 ml of latex liquid (Cementone-Beaver, Buckingham, United Kingdom). We used the ‘Atlantis platform' ( Spooner et al. 1994 ), a polystyrene platform that becomes available by rising from the bottom of the pool only if the animals swim to and stay within a specified ‘dwell radius' centred on the correct location for a predetermined ‘dwell time.' When risen, the top of the platform remained 1.5 cm below the water surface. The animals' swimming was monitored by an overhead video camera connected to a video recorder and an online data acquisition system (Watermaze, Watermaze Software, Edinburgh, United Kingdom; Spooner et al. 1994 ) located in an adjacent room. This system digitizes the path taken by an animal and computes various parameters such as escape latency, time spent in a zone overlying the platform, and other conventional measures of watermaze performance.

Training protocol.

Testing was carried out according to the schedule illustrated in Figure 1 .

Cued pretraining.

This phase consisted of a single day of nonspatial cued training in the ‘upstairs' watermaze (curtains drawn around the pool to occlude extramaze cues, with ten trials in two sessions of five trials each (intertrial interval ≈ 20 min; intersession interval ≈ 3 h). The visible cue was suspended approximately 25 cm above the platform, which was moved every two trials to one of four possible locations, according to a pseudorandom schedule; the dwell radius was set at 20 cm, and the dwell time was 1 s.

Training on a spatial reference memory task began 3 d later in the same watermaze. Rats received ten trials/day, in two sessions of five consecutive trials each (intersession interval ≈ 2 h), for 4 d. The dwell time was set to 0.5 s throughout training, but the dwell radius was gradually reduced over days (day 1: 20 cm; day 2: 15 cm; days 3 and 4: 13 cm). This schedule was intended to promote accurate and focused searching, but without generating the highly perseverative strategy that typically results from the use of long dwell times ( Riedel et al. 1999 ). Rats were given a maximum of 120 s to find an escape platform located at the centre of either the NE or SW quadrant, after which they remained on the platform for 30 s On the rare trials in which a rat failed to escape within 2 min, the experimenter placed a hand above the correct location in order to guide the animal to the platform. For each animal, the platform position remained constant throughout training, but start locations (N, S, E, or W) were varied pseudorandomly across trials. Only those animals achieving the acquisition criterion of mean escape latencies of 15 s or less on day 4 of training proceeded to the next phase of testing.

Surgery took place 1–2 d after the end of training. Rats were given either partial or complete bilateral neurotoxic lesions of the hippocampal formation (DG and CA fields), or sham surgery. Complete lesions were intended to remove 85% or more of the total hippocampal volume. Partial lesions targeted the temporal two-thirds of the hippocampus, sparing the septal (dorsal) third of the structure. The rats were assigned to groups of equivalent mean performance on the basis of their escape latencies during the final day of training. Lesions were made with ibotenic acid (Biosearch Technologies, Novato, California, United States; dissolved in 0.1 M phosphate-buffered saline [pH 7.4] at 10 mg/ml) following the protocol of Jarrard (1989) . The animals were anaesthetized with an intraperitoneal injection of tribromoethanol (avertin) and placed in a Kopf Instruments (Tujunga, California, United States) stereotaxic frame such that Bregma and Lambda lay on the same horizontal plane. Rats received nine or 13 injections of ibotenic acid (partial and complete lesion groups, respectively; 0.05 μ1, 0.08 μ1, or 0.1 μ1 per injection) at different rostrocaudal and dorsoventral levels via an SGE syringe secured to the stereotaxic frame (see de Hoz et al. 2003 ). The injection rate was 0.1 μ1/min, and the needle was removed very slowly 90 s after the injection. A total of 0.65 μ1 or 0.91μ1 per hemisphere was necessary for the partial and complete lesions, respectively. The coordinates were modified from Jarrard (1989) to suit the slightly different brain size of Lister Hooded rats and to achieve the desired amount of partial hippocampal damage (see de Hoz et al. 2003 ). Sham lesions were made in the same way, with the injections replaced by a piercing of the dura (intended to cause comparable neocortical damage).

Retention testing.

This phase began 14 d after the end of training. It consisted of two PTs (PT1and PT2) spaced 1 h apart, with a reminder treatment occurring at the end of PT1.

Each PT (PT1 and PT2) began with a standard 60-s swim with the platform unavailable. In each PT, the rats were placed into the pool in either the adjacent right or the adjacent left quadrants with respect to the training quadrant. Start positions were counterbalanced across PTs and across rats. At the end of the 60 s the platform was raised and the animals were allowed to find and climb onto it. The rats were allowed a further 60 s to locate the platform once risen (but still hidden just below the water surface); if unsuccessful within this period, they were guided to the platform. They then remained on the platform for 30 s.

The raising of the platform at the end of PT1 constituted the reminder treatment; thus PT1 is sometimes referred to as the ‘reminder trial.' A key variable was that the platform was raised in either the original training location (half the animals) or in a novel location in the centre of the opposite quadrant of the pool (the other half). Note that reminding using the original location always occurred in the training quadrant, and reminding using the novel location always occurred in the opposite quadrant. However, whereas the terms ‘training' and ‘opposite' are used to refer to physical areas of the pool, ‘novel' and ‘original' refer also to separate groups that received each type of reminder.

For analysis of the different behavioural phases, several measures of performance were assessed, including escape latency, swim speed, and time spent within defined regions of the pool. Memory retention during PTs is inferred from the time spent in each quadrant of the pool as a percentage of the 60-s duration of the PT. A more sensitive measure can be obtained by analysing percentage time spent within a specified radius (zone) centred on the platform location ( Moser and Moser 1998 ). When time in zone is presented, it is expressed as a percentage of the total time spent in both the original training zone and the novel opposite zone. Statistical analysis (SPSS, Chicago, Illinois, United States) began with an ANOVA followed by appropriate post hoc comparisons. Numerical data are reported as mean ± standard error (s.e.m.) throughout.

Novel context learning.

New learning was assessed the next day in a separate ‘downstairs' watermaze that constituted a novel context. The protocol was identical to that used during ‘upstairs' retention testing, i.e., two rewarded PTs (PT n 1 and PT n 2) spaced 1 h apart.

Lesion analysis.

At the end of behavioural testing, rats were perfused intracardially with saline followed by 10% formalin under terminal pentobarbitone anaesthesia (Euthatal, 1 ml). Their brains were removed and stored in 10% formalin for 24 h before being blocked and embedded in egg yolk. The embedding procedure is described in de Hoz et al. (2003) . Coronal, 30-μm sections through the hippocampus and other structures were cut using a cryostat: every fifth section was recovered, mounted on a slide, and stained with cresyl violet (see Figure 3 A– 3 C).

The relative volume of spared tissue was calculated by measuring the area of hippocampus spared in each section of a particular brain according to the following protocol: Each coronal section containing hippocampus was placed under a photomacroscope (Wild, Heerbrugg, Switzerland), and the image taken by a mounted video camera was imported into NIH Image 1.63 (National Institutes of Health, Bethesda, Maryland, United States). The area of spared hippocampal tissue in each section was then outlined and automatically calculated. Surrounding fibres such as the fimbria were excluded on the grounds that they would not be considered in a section were all the hippocampal cells dead. The sections were spaced 150 μm apart, yielding up to 32 sections in a sham lesion animal, and fewer in animals with acceptable partial lesions. For each rat, the total hippocampal ‘volume' was calculated by adding the area of hippocampal tissue spared in each successive section. The proportion of hippocampus spared for each lesioned animal was expressed as a percentage of the mean hippocampal ‘volume' for sham-lesioned animals. Values for the left and right hippocampi were initially calculated separately and then averaged (see Figure 3 D).

Strict criteria for acceptance of a lesion were used. The lesion had to be confined to the hippocampus in all cases, and leave intact tissue volumes of 25%–50% in the septal hippocampus with minimal sparing (less than 10%) elsewhere in the structure in the case of partial lesions, or less than 15% total hippocampal sparing in the case of complete lesions. Animals with minimal subicular damage, typically located at medial levels of the structure, were accepted.

Acknowledgments

We would like to thank Jane Knox for histology, Andrew Bernard for animal care, and David Foster for helpful discussion. This research was supported by a Medical Research Council (MRC) Programme Grant held by RGMM and an MRC Research Fellowship held by LdH.

Author Contributions

LdH, SJM, and RGMM conceived and designed the experiments. LdH and SJM performed the experiments. LdH and SJM analysed the data. LdH, SJM, and RGMM wrote the paper.

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Title: when to retrieve: teaching llms to utilize information retrieval effectively.

Abstract: In this paper, we demonstrate how Large Language Models (LLMs) can effectively learn to use an off-the-shelf information retrieval (IR) system specifically when additional context is required to answer a given question. Given the performance of IR systems, the optimal strategy for question answering does not always entail external information retrieval; rather, it often involves leveraging the parametric memory of the LLM itself. Prior research has identified this phenomenon in the PopQA dataset, wherein the most popular questions are effectively addressed using the LLM's parametric memory, while less popular ones require IR system usage. Following this, we propose a tailored training approach for LLMs, leveraging existing open-domain question answering datasets. Here, LLMs are trained to generate a special token, <RET>, when they do not know the answer to a question. Our evaluation of the Adaptive Retrieval LLM (Adapt-LLM) on the PopQA dataset showcases improvements over the same LLM under three configurations: (i) retrieving information for all the questions, (ii) using always the parametric memory of the LLM, and (iii) using a popularity threshold to decide when to use a retriever. Through our analysis, we demonstrate that Adapt-LLM is able to generate the <RET> token when it determines that it does not know how to answer a question, indicating the need for IR, while it achieves notably high accuracy levels when it chooses to rely only on its parametric memory.

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vAttention: Dynamic Memory Management for Serving LLMs without PagedAttention

• Ramya Prabhu ,
• Ajay Nayak ,
• Jayashree Mohan ,
• Ramachandran Ramjee ,
• Ashish Panwar

Efficient use of GPU memory is essential for high throughput LLM inference. Prior systems reserved memory for the KV-cache ahead-of-time, resulting in wasted capacity due to internal fragmentation. Inspired by OS-based virtual memory systems, vLLM proposed PagedAttention to enable dynamic memory allocation for KV-cache. This approach eliminates fragmentation, enabling high-throughput LLM serving with larger batch sizes. However, to be able to allocate physical memory dynamically, PagedAttention changes the layout of KV-cache from contiguous virtual memory to non-contiguous virtual memory. This change requires attention kernels to be rewritten to support paging, and serving framework to implement a memory manager. Thus, the PagedAttention model leads to software complexity, portability issues, redundancy and inefficiency.

In this paper, we propose vAttention for dynamic KV-cache memory management. In contrast to PagedAttention, vAttention retains KV-cache in contiguous virtual memory and leverages low-level system support for demand paging, that already exists, to enable on-demand physical memory allocation. Thus, vAttention unburdens the attention kernel developer from having to explicitly support paging and avoids re-implementation of memory management in the serving framework. We show that vAttention enables seamless dynamic memory management for unchanged implementations of various attention kernels. vAttention also generates tokens up to 1.97x faster than vLLM, while processing input prompts up to 3.92x and 1.45x faster than the PagedAttention variants of FlashAttention and FlashInfer.

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Researchers link disturbances in metal to memory

T he study of human consciousness and the brain has long posed questions and frustrations among neuroscientists who said that getting a hold of all the factors was “insurmountable.”  Now, biochemist Dr. Gerard Marx of the private MX Biotech company and chemistry Prof. Chaim Gilon of the Jerusalem Brain Community Brain of the Hebrew University of Jerusalem (HU) have proposed a novel idea – that memory plays a key role in shaping consciousness. 

“We discovered that certain metals binding within the matrix can alter its structure, forming complexes that serve as the fundamental units of memory. These metal complexes can interact with neurotransmitters, resulting in the formation of emotional memory units,” they wrote.

“These memory units collectively create a framework for storing information in the brain. This proposed mechanism sheds light on how disturbances in metal levels could potentially impact memory functions. Furthermore, we speculate that disorders such as Alzheimer 's and autism may be linked to dysregulation of metal handling by the body. Understanding these intricate relationships provides insight into the processes of memory formation and retrieval, aiding in comprehension of conditions ranging from short-term memory loss to more severe memory impairments,” Marx and Gilon continued.

Researching Tripartite Mechanism of Memory 

They explained that their research on the Tripartite Mechanism of Memory investigates the collaborative roles of neurons, the neural extracellular matrix, trace metals, and neurotransmitters in memory formation, storage, and retrieval.

They negated the idea that computer-based “information theory” provides a sufficient basis for understanding neural memory and argued that the emotional content stored within the neural network diverges from standard computer data, laying the foundation for neural memory and adding depth and significance to conscious experience. Their ideas combine the theories of the Global Neuronal Network (GNW) and the Tripartite Mechanism of Memory to offer new insights into the relationship between consciousness and memory. 

They propose a novel perspective that memory underpins consciousness, challenging conventional beliefs. Their model introduces the concept of a “brain cloud” to show how information flows within the brain and highlights a three-step process for neural memory involving neurons, the extracellular matrix, and trace metals. 

They also suggested that bacterial chemical signaling has played a major role in the evolution of memory and consciousness in complex organisms – significantly contributing to advancing our understanding of consciousness and memory and providing a valuable framework for further exploration in the fields of neuroscience and beyond.

In a new paper published in the International Journal of Psychiatry Research under the title “Consciousness as a Fusion of the Global Neuronal Network (GNW) Hypothesis and the Tripartite Mechanism of Memory,” the study provides fresh perspectives on the complex phenomena of consciousness and memory.

MX Biotech works on microfluidic gene-editing for cell therapy, claiming that it transforms cancer immunotherapy , cellular engineering, and genome editing with its patented non-viral genome technology.  

The authors of the article suggested that integrating the GNW theory with the Tripartite Mechanism of Memory leads to better understanding of how the brain creates experiential memories. In their model, they maintain that the complex electro-chemical activities of individual neurons are unified by the structural units of the brain, creating a unified network that facilitates consciousness through emotional memory.

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• Published: 15 April 2023

The critical importance of timing of retrieval practice for the fate of nonretrieved memories

• Verena M. Kriechbaum 1 &
• Karl-Heinz T. Bäuml 1  

Scientific Reports volume  13 , Article number:  6128 ( 2023 ) Cite this article

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Retrieval practice performed shortly upon the encoding of information benefits recall of the retrieved information but causes forgetting of nonretrieved information. Here, we show that the forgetting effect on the nonretrieved information can quickly evolve into recall enhancement when retrieval practice is delayed. During a time window of twenty minutes upon the encoding of information, the forgetting effect observed shortly after encoding first disappeared and then turned into recall enhancement when the temporal lag between encoding and retrieval practice was prolonged. Strikingly, recall enhancement continued to emerge when retrieval practice was postponed up to one week. The results illustrate a fast transition from the forgetting of nonretrieved information to recall enhancement. This fast transition is of relevance for daily life, in which retrieval is often selective and delayed.

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Introduction.

Retrieval is not a neutral event that just measures the products of a previous learning experience. Rather, retrieval changes memory, as illustrated by the wealth of research establishing that retrieval can improve memory for the retrieved information 1 , 2 , 3 , 4 . However, when retrieving encoded information in daily life, retrieval is often selective and only part of the originally encoded information is retrieved—be it in eyewitness testimony situations, educational settings, or many everyday situations, like family conversations over dinner. It is therefore critical to know if retrieval also influences memory for nonretrieved information.

There is evidence that when participants study material and then practice retrieval of a subset of the material, recall of the other, nonretrieved material is often worse than is recall of studied items in the absence of such retrieval practice 5 , 6 , 7 , 8 . The finding thus suggests that retrieval can cause forgetting of nonretrieved information, i.e., information that participants during retrieval practice are not asked to retrieve. However, a feature shared by most of the studies demonstrating such retrieval-induced forgetting has been that retrieval practice followed shortly upon study, with a temporal lag of typically one or two minutes between study and retrieval practice. Employing lag intervals of, for instance, one or two days between study and retrieval practice, more recent research reported other results and found retrieval practice to enhance recall of nonretrieved material 9 , 10 , 11 .

To date, the studies suggesting retrieval-induced forgetting and the studies suggesting retrieval-induced recall enhancement represent rather separate research lines that also differ in potentially critical experimental detail 12 , 13 . It is therefore unclear how exactly the two opposing effects of retrieval practice are related and whether, for instance, the forgetting effect can evolve into recall enhancement when temporal lag between study and retrieval practice is gradually increased from short to longer temporal lag. In such case, the forgetting effect observed shortly after study should first turn into a neutral effect of retrieval practice and then into recall enhancement.

Such transition between the two opposing effects is suggested by a recent view on the effects of retrieval practice 9 , 14 . This view states that selective retrieval can trigger inhibition and blocking as well as context retrieval processes, each of which can influence recall of nonretrieved information. Inhibition operates to attenuate possible interference from the other, nonretrieved items during retrieval practice, thus reducing recall of these items 5 , 6 , 15 . Recall of these items may also be reduced because retrieval practice strengthens the practiced items, which can block recall of the nonretrieved items at test 16 , 17 , 18 . In contrast, context retrieval operates to reactivate study context, which can serve as a retrieval cue and benefit also recall of the nonretrieved items.

Temporal context—the current pattern of activity in an individual`s mind that can be influenced by environmental as well as internal factors—changes gradually over time 19 , 20 . Because each studied item is associated with the temporal context in which it is shown, context during study and context at test will often differ and context at retrieval thus not be the optimal cue for studied items. However, context during recall changes in response to recall attempts 21 , 22 : Recall of an item results in partial reactivation of the context that was present when that item was studied, and this retrieved context then serves as a retrieval cue for other items that had a similar context at study, facilitating recall of these items 21 , 22 , 23 , 24 .

Critically, the relative contribution of context retrieval to recall should be small shortly after study when temporal context is still similar to study context, but it should increase as temporal lag between study and retrieval practice increases and temporal context becomes dissimilar to study context (Fig.  1 ). A gradually increasing lag between study and retrieval practice may thus induce a transition from the forgetting effect caused by inhibition and blocking shortly after study into a neutral effect and then the enhancement effect of retrieval practice. It is the primary goal of this study to demonstrate such transition, which will also provide critical information on how narrow the time window after study is during which retrieval produces forgetting and what the time frame is during which retrieval produces recall enhancement. Such information will impose important restrictions on theories of memory retrieval and create suggestions on how selective retrieval influences memory in daily life, be it in eyewitness testimony or educational situations.

Effects of retrieval practice on the nonretrieved items, i.e., the items that participants during retrieval practice were not asked to retrieve. Hypothetical relative contributions of inhibition, blocking, and context retrieval to recall of the nonretrieved items are shown as a function of temporal lag between study and retrieval practice. After short lag—when temporal context is still similar to study context—the relative contributions of inhibition and blocking are high and that of context retrieval is low, inducing forgetting of the nonretrieved items. When temporal lag increases—and temporal context gets dissimilar to study context—the contribution of context retrieval also increases, which turns the forgetting effect into a neutral and then an enhancement effect on recall of the nonretrieved items.

Here, results from two experiments are reported aimed at shedding light onto whether the forgetting effect of retrieval practice transforms into recall enhancement when temporal lag between study and retrieval practice is gradually increased from short to longer lag interval. In both experiment 1 and experiment 2, recall of nonretrieved items after retrieval practice was compared with recall of studied items when a triplets ordering task serving as a control rather than retrieval practice preceded the recall test (Fig.  2 ). During retrieval practice, some studied items were retrieved, creating retrieved and nonretrieved items, i.e., items that participants during retrieval practice were not asked to retrieve. During the triplets ordering task, participants were presented number triplets and were asked to order each triplet from highest to lowest number. Recall of studied and nonretrieved items was compared for a short 2-min and a longer 20-min lag between study and retrieval practice as well as two intermediate (experiment 1) or one intermediate (experiment 2) lag interval(s). Results from a third experiment are also reported investigating whether the enhancement effect of retrieval practice still emerges when lag interval is prolonged up to one whole week.

Experimental design for experiments 1 and 2. Two groups of participants studied a list of words. ( a ) Recall of one group was tested after retrieval practice of some of the items, which took place after temporal lags of 2, 8, 14, or 20 min after study in experiment 1, and after temporal lags of 2, 11, or 20 min after study in experiment 2, and created retrieved and nonretrieved items. Different subgroups of the group were tested in the different lag conditions. ( b ) Recall of the other group was tested in the absence of retrieval practice after a triplets ordering task serving as a control. Different subgroups of the group engaged in the task 2, 8, 14, or 20 min after study in experiment 1, and 2, 11, or 20 min after study in experiment 2.

Experiments 1 and 2

In each experiment, participants studied a list of items and were later tested on the list. Participants were divided into two groups to understand how retrieval practice influences recall of the nonretrieved items. Recall of the one group was tested after preceding retrieval practice, which took place 2, 8, 14, or 20 min after study in experiment 1, and 2, 11, or 20 min after study in experiment 2. Different subgroups were tested in the different lag conditions. Recall of the other group was tested in the absence of retrieval practice. The group was also divided into different subgroups and each subgroup engaged into the triplets ordering (distractor) task 2, 8, 14, or 20 min after study in experiment 1, and 2, 11, or 20 min after study in experiment 2. After retrieval practice, and after the triplets ordering task, participants were tested on the initially encoded items. During the lag intervals, participants engaged in cognitive (distractor) tasks that were unrelated to the memory task. Different sets of tasks were used in the two experiments, each task being similar to tasks used in prior work on retrieval practice effects (Supplementary Information).

In all three experiments, variance of recall rates did not differ across conditions, as indicated by the results of Levene`s tests. This held when analyzing the effects of temporal lag and item type for the nonretrieved and the studied items (experiment 1: P  = 0.190, experiment 2: P  = 0.890, experiment 3: P  = 0.190) and when analyzing the effects of temporal lag for the retrieved items (experiment 1: P  = 0.723, experiment 2: P  = 0.367, experiment 3: P  = 0.452). We therefore employed analysis of variance and post-hoc t-tests to analyze how recall rates varied across conditions. In experiment 1, typical time-dependent forgetting emerged for the studied items in the absence of retrieval practice, with recall of the items declining from the short to the longer lag conditions, whereas the opposite pattern was present after retrieval practice, with recall of the nonretrieved items increasing as the lag interval increased (Fig.  3 a). Consistently, a two-way analysis of variance with the between-participants factors of lag condition and item type revealed no main effect of lag condition ( F (3, 216) = 0.79, P  = 0.501, η 2  = 0.01) and no main effect of item type ( F (1, 216) = 0.76, P  = 0.384, η 2  < 0.01), but a significant interaction between the two factors ( F (3, 216) = 10.73, P  < 0.001, η 2  = 0.13). Critically, retrieval practice impaired recall of the nonretrieved items relative to recall of the studied items after the short 2-min lag (two-tailed t-test: t (54) = 3.75, P adj  = 0.004, d  = 1.00, 95% CI of the difference = [− 40.58, − 12.28]), but it improved recall of the nonretrieved items after the longer 20-min lag (two-tailed t-test: t (54) = 3.67, P adj  = 0.003, d  = 0.98, 95% CI of the difference = [10.06, 34.23]). In the intermediate lag conditions, no effects of retrieval practice arose (8-min condition, two-tailed t-test: t (54) = 1.21, P adj  = 0.464, d  = 0.32, 95% CI of the difference = [− 4.70, 18.98], B 01  = 3.56; 14-min condition, two-tailed t-test: t (54) = 0.13, P adj  = 0.896, d  = 0.04, 95% CI of the difference = [− 11.62, 10.20], B 01  = 7.39). To control the familywise error rate across the four comparisons, P -values were adjusted by employing the sequential Bonferroni procedure.

Results of experiment 1 ( a ) and experiment 2 ( b ). Recall of the studied items decreased but recall of the nonretrieved items increased from the shorter to the longer temporal lag conditions. After the short 2-min lag, recall of the studied items was superior to recall of the nonretrieved items; after the longer 20-min lag, the pattern reversed and recall of the nonretrieved items was superior to recall of the studied items; recall of the two item types was similar in the intermediate lag conditions. In experiment 2, recall of the nonretrieved items after the 20-min lag resembled recall of studied items when the studied items were tested immediately after study (indicated by the dashed line in ( b ). Error bars represent ± 1 SE.

In experiment 2, recall of the studied items again decreased and recall of the nonretrieved items again increased from the short to the longer lag conditions (Fig.  3 b), thus mimicking recall of the two item types in experiment 1. Again, a two-way analysis of variance with the between-participants factors of lag condition and item type revealed no main effect of lag condition ( F (2, 162) = 0.09, P  = 0.919, η 2  < 0.01) and no main effect of item type ( F (1, 162) = 1.14, P  = 0.287, η 2  < 0.01), but a significant interaction between the two factors ( F (2, 162) = 10.90, P  < 0.001, η 2  = 0.12). Critically, retrieval practice impaired recall of the nonretrieved items relative to recall of the studied items after the short 2-min lag (two-tailed t-test: t (54) = 2.72, P adj  = 0.018, d  = 0.73, 95% CI of the difference = [− 27.30, − 4.13]), but it improved recall of the nonretrieved items after the longer 20-min lag (two-tailed t-test: t (54) = 4.02, P adj  = 0.003, d  = 1.07, 95% CI of the difference = [12.52, 37.48]). In the intermediate lag condition, no effect of retrieval practice arose (11-min condition, two-tailed t-test: t (54) = 0.33, P adj  = 0.744, d  = 0.09, 95% CI of the difference = [− 15.22, 10.93], B 01  = 7.10). Like in experiment 1, the P -values for all three comparisons were adjusted by using the sequential Bonferroni procedure. In both experiment 1 and experiment 2, the forgetting induced by retrieval practice after short lag thus first turned into a neutral effect of retrieval practice, and then into recall enhancement as temporal lag was increased from 2 to 20 min. The transition was largely unaffected by the different cognitive tasks participants engaged in during the lag intervals in experiments 1 and 2.

The fact that retrieval practice enhanced recall of the nonretrieved items relative to the studied items after the 20-min lag implies that retrieval practice attenuated the items' time-dependent forgetting. To provide insight into whether retrieval practice even eliminated the items' forgetting over time, in experiment 2, recall of the nonretrieved items after the 20-min lag was compared to recall of studied items when these items were tested immediately after study in the absence of retrieval practice and in the absence of the triplets ordering task. Recall of the nonretrieved items was similar to recall in this immediate recall condition (two-tailed t-test: t (54) = 0.88, P  = 0.383, d  = 0.24, 95% CI of the difference = [− 7.31, 18.74], B 01  = 5.01), suggesting that retrieval practice largely eliminated the items' forgetting over time.

In contrast to the studied and the nonretrieved items, recall of the retrieved items did not vary with temporal lag (experiment 1, one-way ANOVA: F (3, 108) = 0.10, P  = 0.961, η 2  < 0.01; experiment 2, one-way ANOVA: F (2, 81) = 0.31, P  = 0.733, η 2  < 0.01) and thus followed the items' recall during retrieval practice (Supplementary Information, Supplementary Tables 1 – 2 ).

Experiment 3

The fact that retrieval practice largely eliminated nonretrieved items' forgetting over time when retrieval practice occurred 20 min after study suggests that retrieval practice reinstated study context more or less completely, thus making recall after retrieval practice comparable to recall directly after study. However, reinstating study context may get harder if the lag interval between study and retrieval practice is increased up to hours or even days. For such prolonged lag intervals, only part of the accumulated time-dependent forgetting may therefore be eliminated. Using similar experimental setup as was employed in experiments 1 and 2, experiment 3 was aimed at examining the effects of retrieval practice for lag intervals of 2 h, 2 days, and 7 days. In all three lag conditions, recall of the nonretrieved items after retrieval practice was compared to recall of the studied items when participants were engaged in the triplets ordering task prior to the recall test. Following experiment 2, recall of the nonretrieved items was also compared to recall of studied items when these items were tested immediately after study in the absence of retrieval practice and in the absence of the triplets ordering task.

For both the studied items and the nonretrieved items, typical time-dependent forgetting emerged, with recall of the nonretrieved items after retrieval practice being superior to recall of the corresponding studied items (Fig.  4 ). A two-way analysis of variance with the between-participants factors of lag condition and item type found main effects of lag condition ( F (2, 162) = 9.00, P  < 0.001, η 2  = 0.10) and item type ( F (1, 162) = 37.16, P  < 0.001, η 2  = 0.19), but no significant interaction between the two factors ( F (2, 162) = 0.17, P  = 0.846, η 2  < 0.01), suggesting that retrieval practice can enhance recall of the nonretrieved items also after temporal lags of hours and even days and does so to a similar degree across conditions. In the 2-h and 2-d lag conditions, recall of the nonretrieved items was even similar to recall in the immediate recall condition (2-h condition, two-tailed t-test: t (54) = 0.40, P adj  = 0.691, d  = 0.11, 95% CI of the difference = [− 8.61, 12.90], B 01  = 6.93; 2-d condition, two-tailed t-test: t (54) = 2.17, P adj  = 0.068, d  = 0.58, 95% CI of the difference = [− 21.97, − 0.88], B 01  = 0.71), thus mimicking results in the 20-min lag condition of experiment 2 and indicating that, also after hours, retrieval practice can largely eliminate nonretrieved items' forgetting over time. In contrast, in the 7-d lag condition, recall of the nonretrieved items was inferior to recall in the immediate recall condition (two-tailed t-test: t (54) = 4.02, P adj  = 0.003, d  = 1.08, 95% CI of the difference = [− 31.04, − 10.39]), suggesting that retrieval practice eliminated only part of the accumulated time-dependent forgetting. For all three comparisons of the nonretrieved items' recall rates to the immediate recall condition, P -values were again adjusted following the sequential Bonferroni procedure. With the larger range of lag intervals employed in this experiment relative to experiments 1 and 2, recall of the retrieved items also decreased with temporal lag (one-way ANOVA: F (2, 81) = 9.75, P  < 0.001, η 2  = 0.19). Like in experiments 1 and 2, recall of the retrieved items at test resembled the items’ recall during retrieval practice (Supplementary Information, Supplementary Table 3 ).

Results of experiment 3. Both recall of the studied items and recall of the nonretrieved items decreased with increasing temporal lag. In all three lag conditions, recall of the nonretrieved items was superior to recall of the studied items. Recall of the nonretrieved items after the 2-h lag resembled recall of studied items when the studied items were tested immediately after study (indicated by the dashed line), whereas, after the 2-d and 7-d lags, recall of the nonretrieved items was inferior to recall in this immediate recall condition. Error bars represent ± 1 SE.

This study demonstrates that the forgetting that retrieval practice produces for the nonretrieved material when it occurs shortly upon study can evolve into recall enhancement. The observed forgetting first turns into a neutral effect of retrieval practice and then into recall enhancement if the temporal lag between study and retrieval practice gradually increases from short to longer lag interval. Prior work had already demonstrated enhancement effects on the nonretrieved items after longer lag 9 , 10 , 11 , 23 , but retrieval practice was mostly part of the test phase in these studies and recall of studied items was measured in the presence versus absence of the preceding recall ("retrieval practice") of other studied items. This study shows that the enhancement effect also arises when retrieval practice and test are separated into distinct experimental phases and, while participants in the retrieval-practice condition engage in retrieval practice, participants in the no-retrieval-practice condition engage in an unrelated cognitive task of equal duration as a control, which has become the standard paradigm to study the forgetting effect of retrieval practice 8 , 12 , 25 .

The results illustrate that the transition from retrieval-produced forgetting into recall enhancement can be fast. Typical forgetting of the nonretrieved items emerged when retrieval practice occurred 2 min after study, but the forgetting quickly disappeared when temporal lag between study and retrieval practice was increased. Recall of the nonretrieved items was more or less unaffected by retrieval practice when practice took place about 10 min after study, and another 10 min later, retrieval practice already led to recall enhancement. Strikingly, the recall enhancement observed 20 min after study was sufficiently strong to eliminate the time-dependent forgetting that had accumulated since study. Retrieval practice thus effectively protected the nonretrieved items from showing forgetting over time.

These findings are consistent with the idea that context retrieval critically contributes to recall when retrieval practice is delayed 9 , 14 , 24 . Shortly upon study, when temporal context is still similar to study context, recall can not benefit much from context retrieval but inhibition and blocking operate in response to retrieval practice, which causes forgetting of the nonretrieved items. However, as time after study passes and context gets more and more dissimilar to study context, recall benefits from context reactivation and context retrieval enhances recall of the nonretrieved items. Critically, the finding that 20 min after study retrieval practice eliminated the time-dependent forgetting that had accumulated since study does not only suggest that study context reactivation was more or less complete, it also indicates that inhibition and blocking barely contributed to recall at this point in time. Retrieval practice therefore caused mainly inhibition and blocking shortly after study, and mainly context retrieval about 20 min later.

The finding that, in both experiment 1 and experiment 2, the beneficial effect of retrieval practice arose when retrieval practice was delayed by 20 min shows that the difference in cognitive (distractor) tasks between the two experiments did not much influence the results. Still, type of distractor task may affect results. For instance, if participants were engaged in daydreaming distractor tasks, which have been shown to enhance internal context change 26 , 27 , context retrieval may play a stronger role for recall than it did here and the beneficial effect of retrieval practice thus arise even earlier 28 . Item lists may also influence results. For instance, if item lists were presented to participants that induced a higher level of interitem interference than the lists employed here, the amount of blocking and inhibition on the nonretrieved items may be enhanced and the beneficial effect arise somewhat later. Future work is required to pin down the range of possible cross-over points between the detrimental and beneficial effects as well as the range of possible lag intervals after which the beneficial effect arises. Likely, such results will show that, in general, the beneficial effect emerges after rather short lag between study and retrieval practice, a lag interval in the order of minutes, not of hours or even days.

The results also reveal that context retrieval contributed to recall when retrieval practice took place 2 h, 2 days, and even 7 days after study, again enhancing recall of the nonretrieved items. However, whereas retrieval practice again protected the nonretrieved items from showing forgetting over time when it occurred 2 h after study, only part of the time-dependent forgetting was eliminated when lag interval increased to seven days, indicating that study context reactivation can become incomplete after very long lag 23 , 29 . The change from complete to incomplete elimination of time-dependent forgetting was accompanied by a reduction in recall success during retrieval practice (Supplementary Information), which fits with the view that recall success during retrieval practice is a critical component for successful study context reactivation 23 .

Study context can not only be reactivated through retrieval practice. Context reactivation can also arise if participants, some time after study, are asked to mentally reinstate study context 23 . Such deliberate active reinstatement attempts can make recall superior relative to a no-reinstatement condition, although, often, they do not lead to perfect study context reactivation 26 , 30 , 31 . Individuals also can maintain and use an older context when they know the task requires so. If individuals learn a series of lists of items and, from second list on, are asked after each list to recall the list prior to the last presented list, they are able to use the context that is appropriate for the prior list rather than the current one, though experience with the task may be required to show the effect 32 , 33 .

The present experiments varied the lag between study and retrieval practice while holding the delay between retrieval practice and test constant and short, which allows to measure possible effects of blocking, inhibition, and context retrieval on the nonretrieved items more or less directly after practice. The present research thus differs from research on the so-called spacing effect, in which the beneficial mnemonic effect of spaced over massed practice on studied material is examined while holding the retention interval between study and test constant 34 , 35 , 36 . Future work may thus bridge the gap between the present research and research on the spacing effect by examining the effects of time-lagged selective retrieval also for constant retention interval.

A number of studies have identified neural correlates of inhibition as induced by retrieval practice. The studies provided evidence for critical roles of both the anterior cingulate cortex and the lateral prefrontal cortex and indicated that retrieval practice indeed suppresses the nonretrieved items' memory representations 37 , 38 , 39 . Lateral prefrontal cortex has also been identified as a possible correlate of blocking processes 40 . Studies investigating neural correlates of context retrieval are relatively scarce to date but suggested roles of lateral prefrontal cortex as well as medial and lateral parietal lobe regions 21 , 41 , 42 . This study offers an experimental setup that may be applied to investigate neural correlates of inhibition and blocking as well as context retrieval when the two types of processes operate mainly in isolation and when they operate in concert. With temporal lag between study and retrieval practice as the critical factor, lags of about 2 min may be used to study inhibition and blocking when context retrieval is more or less absent and lags of at least 20 min may be used to study context retrieval when inhibition and blocking are largely absent. Intermediate temporal lags may reveal possible interactions between the two types of processes.

Retrieval practice after short lag does not always produce forgetting. Coherent study material, for instance, can reduce interference between the single memory entries and make inhibition and blocking obsolete 43 , 44 . In such cases, retrieval practice triggers mainly context retrieval and may improve recall of the nonretrieved material if retrieval practice is performed only few minutes after encoding of the memory entries. Similarly, retrieval practice after longer lag will not always produce recall enhancement. If particularly salient features surrounded an encoded episode, reactivation of such features—be it through reexposure of the features or deliberate active reinstatement attempts—immediately before retrieval practice starts may revive the encoding context and thus reduce the likelihood of further retrieval-induced context retrieval 23 , 45 . Thus, not only lag between study and retrieval practice but also a few other factors can influence whether retrieval practice produces forgetting or recall enhancement for other memory contents.

The experimental task employed here as well as the experimental tasks used in prior work 23 , 24 show some specific features. For instance, during retrieval practice, two-thirds of the studied material are practiced and practice is conducted in two successive practice cycles, features that may enhance the effects of retrieval practice. Or, at test, item-specific retrieval cues are presented and the nonretrieved items are tested before the retrieved items, features that permit rather direct measurement of nonretrieved items’ blocking, inhibition, or context reactivation. Likely, the size of the effects of retrieval practice would decrease only slightly if a smaller proportion of the studied material was practiced or a single practice cycle was conducted only 8 , 9 , 10 . Whether testing the retrieved items first and the nonretrieved items last or an alternative free recall format—in which the (stronger) retrieved items would also tend to be recalled first—would influence results is less clear, but prior recall of the retrieved items could serve as an additional opportunity for blocking, inhibition, and context reactivation and thus potentially increase the effects on the nonretrieved items.

Retrieval practice can trigger inhibition and blocking and cause forgetting of nonretrieved information. This study shows that, when retrieval practice is delayed, it can also trigger context retrieval that reactivates the encoding context and enhances recall of the nonretrieved information. Critically, the transition between forgetting and recall enhancement can be fast. During a time window of twenty minutes upon encoding, the forgetting observed shortly after study first disappeared and then turned into recall enhancement as temporal lag between study and retrieval practice was increased. Strikingly, recall enhancement continued to emerge when retrieval practice was postponed by several days or even one whole week. The findings are of high relevance for daily life, because in the real world retrieval is often selective and it is often delayed. In such situations, retrieval practice may be an effective tool to improve also recall of other, nonretrieved memories.

Experiment 1

Participants. The participants (224 students of different German universities, mean age 23.61 y, 75.9% females) were divided into two groups, each consisting of four subgroups of n  = 28 participants. Sample size was determined on the basis of a power analysis 46 using alpha = 0.05 and beta = 0.20 and effect sizes of d  = 0.80 for expected time-dependent forgetting and expected detrimental and beneficial effects of retrieval practice 9 , 10 , 23 , 24 , 47 . The participants were tested individually in an online video conference hosted by the software Zoom (Zoom Video Communications, 2016). Instructions were given by the experimenter, who was present for the entire period of the experiment.

Materials. A list of 15 unrelated concrete German nouns was employed as study material 23 , 24 . Each item had a unique initial letter. The items served as studied items when retrieval practice was absent and as retrieved and nonretrieved items when retrieval practice was present. Ten items of the list served as the retrieved items and the other five items served as the nonretrieved items. Within each lag condition, each item was a retrieved item for n  = 18 or n  = 19 participants and a nonretrieved item for n  = 9 or n  = 10 participants.

Procedure. Each participant in this experiment—as well as in experiments 2 and 3—provided informed consent prior to participation. The protocol employed in this study was reviewed and deemed exempt by the ethical review board of Regensburg University. The experiments were carried out in accordance with the provisions of the World Medical Association Declaration of Helsinki. During study, the items of the list were presented individually and in a random order for 6 s each on the computer screen. Four different lag intervals (2, 8, 14, and 20 min) followed, filled with cognitive tasks that were unrelated to the memory task, including mental rotation of dices, applied arithmetics, and detecting repetitions of stimulus features in a sequence of visually presented objects (Supplementary Information). In each single lag condition, half of the participants then engaged in retrieval practice, whereas the other half engaged in a triplets ordering task. During retrieval practice, participants were asked to recall 10 of the 15 items (the retrieved items). The items' first two letters served as retrieval cues and were presented in a random order for 6 s each. There were two rounds of practice. During the triplets ordering task, participants were presented number triplets for the equivalent amount of time. They were asked to order each triplet from highest to lowest number. After a subsequent 2-min counting task, all participants were finally asked to recall all 15 items. The items' initial letters served as retrieval cues and were presented in a random order for 6 s each. Order of tested items was random but, in the retrieval practice group, the nonretrieved items were always tested first and the retrieved items last 5 , 12 , 24 , 25 . All responses given by the participants in this experiment were given orally. Because recall performance can vary with items’ output position at test 48 , 49 , 50 , we followed prior work on the effects of selective retrieval practice and compared recall rates of the nonretrieved items with recall rates of studied items tested in the same—i.e., first five—output positions 5 , 24 .

Experiment 2

Participants. Another sample of participants (196 students of different German universities, mean age 24.1 y, 79.1% females) was divided into three groups. Two of the groups engaged in retrieval practice or the triplets ordering task and consisted of three subgroups of n  = 28 participants each. The third group ( n  = 28) did not engage in retrieval practice or triplets ordering.

Materials. Another list of 15 unrelated concrete German nouns was employed as study material 23 , 24 . Again, the items had unique initial letters. The division of the items into studied, retrieved, and nonretrieved items followed experiment 1.

Procedure. The procedure differed in three aspects from experiment 1: (1) The lag intervals after study were changed to 2, 11, and 20 min; (2) a different set of cognitive tasks was employed to fill the single lag intervals, including mental overlaying of visual objects, the operation span task, a progressive matrices test, and a fill-in-the-arithmetic-operators task (Supplementary Information); (3) there was an immediate recall condition, which was identical to the other six conditions with regard to study and test but differed from these conditions in that no retrieval practice and no triplets ordering was conducted; rather, recall was tested directly after study and a 2-min distractor task (and thus 4 min earlier than in the 2-min lag conditions, in which the 2-min lag was followed by 2 min of retrieval practice or triplets ordering and another 2-min distractor task).

Participants. Like in experiment 2, a distinct sample of participants (196 students of different German universities, mean age 24.0 y, 69.4% females) was divided into three groups. Two groups engaged in retrieval practice or the triplets ordering task and consisted of three subgroups of n  = 28 participants each. The third group ( n  = 28) did not engage in these tasks.

Materials. The same material was used as in experiment 2. The division of the items into studied, retrieved, and nonretrieved items followed experiment 1.

Procedure. The procedure differed in one aspect from experiment 2: the lag intervals after study were changed to 2 h, 2 d, and 7 d. In all three lag conditions, a 2-min distractor task followed study and the participants were then dismissed for this lag interval, rejoining the experiment later. The same immediate recall condition was included as in experiment 2.

Data availability

The data from the single experiments as well as the materials employed in the experiments are available on the Open Science Framework ( https://osf.io/x5e3r/?view_only=67ff5f35e12b4b7e80a14b1b71694dba ).

Code availability

All experiments reported in this manuscript were implemented using the software PowerPoint 2019 (Microsoft Corporation) and the software Zoom (Zoom Video Communications Inc., 2016). The software was run on standard desktop computers with the operating system Windows 10 (Microsoft, Redmond, WA). Data were analyzed using IBM SPSS Statistics for Windows, Version 27.0 (IBM Corp., Armonk, NY) and G*Power 3.1 46 .

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Kriechbaum, V.M., Bäuml, KH.T. The critical importance of timing of retrieval practice for the fate of nonretrieved memories. Sci Rep 13 , 6128 (2023). https://doi.org/10.1038/s41598-023-32916-7

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DOI : https://doi.org/10.1038/s41598-023-32916-7

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