memory retrieval research paper

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]

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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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Learning and memory

Anna-katharine brem.

1 Berenson-Allen Center for Noninvasive Brain Stimulation, Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA

ALVARO PASCUAL-LEONE

2 Institut Guttman de Neurorehabilitació, Universitat Autonoma, Barcelona, Spain

INTRODUCTION

A fairly large number of studies to date have investigated the nature of learning and memory processes in brain-injured and healthy subjects with noninvasive brain stimulation (NBS) methods. NBS techniques, such as transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS), can alter brain activity in targeted cortical areas and distributed brain networks. The effects depend on the stimulation parameters. TMS and tDCS can be used to interfere with ongoing brain activity (“virtual lesion”) and thus help to characterize brain–behavior relations, give information about the chronometry of cognitive processes, and reveal causal relationships. Particularly in real-time combination with electroencephalography (EEG) or functional magnetic resonance imaging (fMRI), TMS and tDCS are valuable tools for neuropsychological research. They offer the combination of interference methods (TMS, tDCS) with techniques to record ongoing brain activity with high temporal (EEG) and spatial (MRI) resolution. This can: (1) shed unique insights into physiological and behavioral interactions, and (2) test, refine, and improve cognitive models; and (3) might ultimately lead to better neurorehabilitative methods.

The main goals of research with NBS in learning and memory have been to: (1) identify underlying neuropsychological processes and neurobiological components; (2) find out how this knowledge can be used to diagnose and restore dysfunctions of learning and memory in various patient populations; and (3) assess the use of NBS for enhancement purposes in healthy subjects.

In the present chapter, we first review and define memory and learning processes from a neuropsychological perspective. Then we provide a systematic and comprehensive summary of available research that investigates the neurobiological substrates of memory and aims to improve memory functions in patient populations, as well as in healthy subjects. Finally, we discuss methodological considerations and limitations, as well as the promise of the approach.

FRAMING APPLICATION OF NONINVASIVE BRAIN STIMULATION IN THE CONTEXT OF NEUROPSYCHOLOGICAL DEFINITIONS

Learning and memory are cognitive functions that encompass a variety of subcomponents. These components can be structured in different ways. For example, we can focus on their temporal dimension, or differentiate various forms of memory by virtue of their content or mechanisms of acquisition ( Fig. 55.1 ). It seems clear that the cognitive structure of learning and memory is complex, and that, given the many interactions and overlaps between key subcomponents, neither neuropsychological nor neurobiological models can give us a fully satisfying taxonomy.

Classification of different types of memory process.

A key advance in the study of the neurobiological substrates of memory was Squire’s (1987 , 2004 ) distinction between declarative and nondeclarative memory functions related to their differential reliance on distinct neural structures ( Cohen and Squire, 1980 ). Declarative memory incorporates semantic and episodic memory, and refers to everyday memory functions, which are typically impaired in amnesic patients. Declarative memory is thought to rely primarily on medial temporal lobe structures, including the hippocampus. Nondeclarative memory includes various subcomponents, of which procedural memory or formation of motor memories is the most prominent. Nondeclarative memory is thought to depend mostly on striatum, cerebellum, and cortical association areas ( Cohen and Squire, 1980 ). However, procedural memory also includes associative learning forms, such as classical and operant conditioning, and nonassociative learning forms such as priming, habituation, and learning of perceptual and cognitive routines. Notably, motor learning has been regarded as a less cognitive form of memory functions, and most research makes a clear distinction between motor and nonmotor memory functions. Thus, it seems clear that declarative and nondeclarative memory processes are interactive and partly overlapping domains.

Historically, the distinction between explicit and implicit memory has been associated with declarative and nondeclarative memory. It is often argued that declarative memory (semantic and episodic memory) corresponds to explicit memories that are conscious and verbally transmittable. On the other hand, nondeclarative memory is thought to represent an implicit and nonverbal type of memory that is acquired subconsciously. Although most declarative memory contents seem to be acquired explicitly, and most nondeclarative memory contents appear to be acquired implicitly, this dichotomy is an oversimplification and ultimately not accurate. For example, declarative memories can be acquired subconsciously (e.g., memories of an emotionally intense event or subliminal priming effects), and nondeclarative memories can be acquired with conscious engagement (e.g., learning of motor movements playing sports or a musical instrument).

Another important dichotomy, first proposed by William James (1890) , differentiates memory subcomponents along a temporal dimension of duration (short-versus long-term memory, STM versus LTM). Since then researchers have proposed that STM and LTM are dependent on different neural substrates. More recently, however, it has been argued that the same representations that are active during encoding are also active during STM or during retrieval from LTM. According to these models, medial temporal lobe structures are responsible for the establishment of new representations independent of their duration, and the same binding processes are active in both STM and LTM ( Wheeler et al., 2000 ; Jonides et al., 2008 ). A related temporal dichotomy separates retrograde and anterograde memory processes ( Hartje and Poeck, 2002 ; Markowitsch and Staniloiu, 2013 ). Access to memories of the past enables us to improve current decisions, while mental time traveling and the imagination of future experiences helps us to follow long-term goals ( Boyer, 2008 ).

These are some of the complex and not mutually exclusive dichotomies of memory processes that NBS could help link to specific neural substrates. For example, one can conceive of experiments aimed at assessing whether disruption of specific brain regions affects one type of memory process and not another (e.g., Basso et al., 2010 ), or experiments evaluating the time at which disruption of a given brain region interferes with a specific memory step (e.g., Oliveri et al., 2001 ). One can use NBS to explore the nature of the relation between different processes within or across different dichotomies. Finally, one can compare the effects of NBS in healthy individuals and those with deficits in specific memory processes, and evaluate the impact on the deficit or even on other, apparently unaffected, memory and learning types.

It is also apparent that memory is tightly connected to time perception, attention, and emotional valence of memory contents, and there is evidence that brain circuits implicated with these functions are overlapping with areas involved in processing of memory functions. For example, with an increasing load of varying experiences stored in memory, time intervals are perceived to be longer ( Bailey and Areni, 2006 ), and the subjective perception of a long time interval recruits areas such as the medial temporal cortex, which is known to be involved in binding episodic memory features ( Noulhiane et al., 2007 ). State-dependent models have proposed that there is no “centralized clock,” but that there are time-dependent neural changes, such as short-term synaptic plasticity, accounting for the decoding of temporal information ( Karmarkar and Buonomano, 2007 ). It has been suggested that there is no linear metric of time, but that short time intervals are rather encoded in the context of (memory) events and therefore a state of local neural networks. In the same way as long-term plasticity may provide a memory of a learning experience ( Martin et al., 2000 ), state-dependent networks may use short-term plasticity to provide a memory trace of the recent stimulus history of a network ( Buonomano, 2000 ). These are further examples of questions that NBS can help address. Pharmacological experimental interventions suggest that affecting working memory (WM) also interferes with temporal processing ( Rammsayer et al., 2001 ). However, NBS offers a promise of spatial and temporal precision that pharmacological agents lack.

Currently, researchers are trying to integrate findings in the memory domain into comprehensive models aiming to account for the wealth of data on functional characteristics of memory networks. There are debates over the implication of attention functions to memory and specifically, for example, of the role of parietal regions to retrieval of episodic memory. For instance, the Attention to Memory (AtoM) model postulates that the dorsal parietal cortex mediates top-down attention processes guided by retrieval goals (orienting), while ventral parietal cortex mediates automatic bottom-up attention processes captured by retrieved memory output (detection) ( Ciaramelli et al., 2008 ; Cabeza et al., 2011 ). Cabeza and colleagues (2011) have proposed that parietal regions control attention in a similar way to perception processes. While orienting-related activity for memory and perception are thought to overlap in dorsoparietal cortex (DPC), detection-related activity is believed to overlap in ventroparietal cortex (VPC). Furthermore, both DPC and VPC show strong connectivity with medial–temporal lobe (MTL) during a memory task, which can, however, shift to strong connectivity with visual cortex during a perception task. Accordingly, the DPC appears to be collaborating with the prefrontal cortex (PFC) to induce top-down attention to salient retrieval paths, while the VPC seems to be involved in the activation of episodic features in alliance with the MTL. Thus, current models of memory processes integrate dynamic concepts of distributed network interactions and plasticity. These and other conclusions are derived from brain imaging studies, which, although extremely valuable, cannot offer insights into causality ( Silvanto and Pascual-Leone, 2012 ). Here again, NBS offers the promise of a transformative approach.

Procedural memory

Motor learning and the formation of motor memories can be defined as an improvement of motor skills through practice, which are associated with long-lasting neuronal changes. They rely primarily on the primary motor cortex, premotor and supplementary motor cortices, cerebellum, thalamus, and striatal areas ( Karni et al., 1998 ; Muellbacher et al., 2002 ; Seidler et al., 2002 ; Ungerleider et al., 2002 ). As learned from patients with apraxia, the parietal cortex is furthermore implicated in accessing long-term stored motor skills and contributes to visuospatial processing during motor learning ( Halsband and Lange, 2006 ). Frontoparietal networks may become important after learning has been established, and play key roles in consolidation and storage of skill ( Wheaton and Hallett, 2007 ).

Motor learning and memory take a special place within the memory domain and have been studied extensively. However, procedural memories build on subprocesses similar to those of nonmotor memories: they are divided into encoding, consolidation and long-term stability, retrieval ( Karni et al., 1998 ; Robertson et al., 2005 ), and even a short-term memory system has been suggested to exist in the primary motor cortex ( Classen et al., 1998 ). Robertson (2009) has further proposed that motor and nonmotor memory processes may be fully or partially supported by the same neuronal resources during wakefulness, but not during sleep. Indeed, the MTL – which is known to support declarative memory formation – also contributes to implicit procedural learning ( Schendan et al., 2003 ; Robertson, 2007 ; Albouy et al., 2008 ). During sleep, motor and nonmotor memory systems may be functionally disengaged, which may promote independent offline consolidation within systems ( Robertson, 2009 ). As we shall see, key aspects of such insights have been derived from recent studies using NBS.

Short-term memory

STM is an essential component of cognition and is defined as the maintenance of information over a short period of time (seconds). Multistore models differentiate between STM and LTM. STM can remain unimpaired in amnesic patients who show distinct LTM impairments ( Scoville and Milner, 1957 ; Cave and Squire, 1992 ). However, STM can be impaired while LTM functions remain intact ( Shallice and Warrington, 1970 ). According to William James (1890) , STM (primary memory) involves a conscious maintenance of sensory stimuli over a short period of time after which they are not present anymore. On the other hand, LTM (secondary memory) involves the reactivation of past experiences that were not consciously available between the time of encoding and retrieval. This led to the assumption, going back to Hebb (1940s), that STM and LTM are based on separate neural systems. While STM engages repeated excitation of a cellular compound, LTM leads to structural changes on the synaptic level, which are preceded by consolidation processes that are thought to be highly dependent on hippocampal functions. NBS, particularly TMS combined with EEG, MRI, or other brain imaging methods, has provided valuable insights on such neurobiological questions.

Baddeley also proposed a multistore architecture of STM and LTM ( Baddeley and Hitch, 1974 ; Baddeley, 1986 ). In his model, STM consists of a “verbal buffer” (phonological loop) and a “visuospatial sketchpad” (maintenance of visual information). He later added an “episodic buffer” that is supposed to draw on the other buffers and LTM ( Baddeley, 2000 ). Finally, a “central executive” is argued to be responsible for orchestrating all components. As we shall see, such cognitive models lend themselves exquisitely well to hypothesis testing with NBS.

Unitary store models assume that the MTL is engaged in both STM and LTM, and that its function is the establishment of new representations independent of their duration. Accordingly, information that does not require binding processes can be preserved in amnesic patients, which might also explain often preserved retrieval of consolidated preinjury memories. In a comprehensive review, Jonides and colleagues (2008) concluded that STM and LTM are not separable, but that STM consists of temporarily activated LTM representations. Several studies have confirmed these assumptions ( Ranganath and D’Esposito, 2001 ; Hannula et al., 2006 ; Olson et al., 2006a , b ). According to their assumptions, initial neural representations are also the repository of long-term representations, as they are active during encoding, as well as during STM, or the retrieval from LTM into STM ( Wheeler et al., 2000 ). Chronometric brain stimulation experimental designs can be applied to explore such questions (e.g., Mottaghy et al., 2003a ).

Long-term memory

LTM refers to the mechanism by which acquired memories gain stability or are strengthened over time, and become resistant to interference ( Brashers-Krug et al., 1996 ; McGaugh, 2000 ; Dudai, 2004 ). Consolidation is assessed as a change in performance between testing and retesting ( Robertson et al., 2004 ; Walker, 2005 ) and provides a direct measure of “offline” changes.

Mainly two components of LTM are described in the literature and frequently included under the term “declarative memory” – episodic and semantic memory. They rely mostly on MTL structures. Episodic memory refers to contents that can be located within a spatiotemporal context, such as holiday memories or autobiographical events. On the other hand, semantic memories are independent of context and are not personally relevant. They consist of general and factual world knowledge, such as “Dakar is the capital city of Senegal.” However, “nondeclarative” memory functions, such as procedural memory (see above), also involve LTM consolidation processes, such as knowing how to ride a bike.

Successful long-term storage includes several steps starting with the encoding of information, followed by short-term storage and consolidation from STM to LTM, as well as repeated reconsolidation. Consolidation is thought to occur in a structured way allowing for prompt and precise retrieval. Elegant work from Muellbacher and colleagues (2002) pioneered the use of NBS approaches to explore the neurobiology of such processes in humans. During consolidation, memories can undergo changes that can be quantitative (enhancement, strengthening) as well as qualitative in nature (e.g., awareness of underlying sequences) ( Wagner et al., 2004 ; Walker, 2005 ; Robertson and Cohen, 2006 ). Chronometric brain stimulation paradigms are contributing to clarify some of these issues. Consolidation mechanisms may depend on neuronal reactivation (signal increase), on the removal of noise-inducing synaptic changes (noise decrease), or their combination, all of which can be examined with NBS. For example, offline performance changes seem to be causally associated with neuronal reactivation ( Rasch et al., 2007 ). However, it remains to be shown that disruption of reactivation would impair consolidation processes, a problem that seems experimentally approachable with TMS.

It has been shown that sleep plays an important role in the consolidation of memories ( Walker et al., 2002 ; Korman et al., 2007 ), and it has been argued (synaptic homeostasis hypothesis) that a net increase in the efficacy and number of synapses during wakefulness may add noise to the network. The reduction of noise would therefore improve the signal-to-noise ratio. Slow-wave sleep is thought to be responsible for downscaling synaptic strength and therefore noise reduction ( Tononi and Cirelli, 2003 , 2006 ), and has been associated with learning and the induction of brain plasticity ( Huber et al., 2004 , 2006 ; De Gennaro et al., 2008 ). NBS, in this case, particularly tDCS, is being elegantly employed to test some of these notions, while TMS–EEG studies are providing experimental support for the underlying hypotheses (e.g., Marshall et al., 2004 , 2011 ).

Encoding and retrieval

During encoding, various event features distributed across neocortical areas are held actively online through processes guided by the PFC ( Miller and Cohen, 2001 ; D’Esposito, 2007 ). TMS and tDCS lend themselves well to experimentally test such notions and evaluate precise spatial and temporal aspects of the hypothesized neural substrates.

The MTL is thought to be responsible for binding these representations in a highly structured way to enable optimal retrieval at a later timepoint ( Cohen and Eichenbaum, 1991 ; Squire and Zola, 1998 ), and activity in PFC and MTL during encoding is correlated with successful retrieval ( Paller and Wagner, 2002 ). Moreover, intermediate processes such as additional encoding or consolidation processes, are relevant for further stabilization of memories ( Squire, 1984 ; Nadel et al., 2000 ; Paller, 2002 ). Critical encoding components include bottom-up sensory processes as well as top-down processes that select/engage, maintain, and update relevant features ( Shimamura, 2011 ). Here again, NBS is a valuable experimental tool, thanks to the opportunity of interference with ongoing neural activity in a spatially and temporally controlled manner.

Retrieval of episodic memories depends on the recollection of encoded contextual features of a past event, such as time, place, people, sights, thoughts, and emotions ( Mitchell and Johnson, 2009 ). Source memory is therefore an important element of episodic memory ( Tulving, 2002 ; Shimamura and Wickens, 2009 ). MTL plays its part in memory retrieval by reinstating these features ( Eldridge et al., 2005 ; Moscovitch et al, 2006 ). Successful retrieval has also been associated with the PFC ( Buckner et al., 1998 ; Dobbins et al., 2002 ; Simons and Spiers, 2003 ), which is involved in top-down executive control. The HERA (Hemispheric Encoding/Retrieval Asymmetry) model proposed by Tulving and colleagues (1994) postulates that both prefrontal lobes subserve memory processes, but play different roles. While the left PFC is believed to be more involved in encoding and semantic retrieval, the right PFC is thought to be more important in episodic memory retrieval. Early functional imaging studies proposed an asymmetry in memory processes irrespective of modality, with encoding and retrieval being associated with left and right/bilateral PFC respectively ( Cabeza and Nyberg, 2000 ; Haxby et al., 2000 ; Fletcher and Henson, 2001 ). The HAROLD (Hemispheric Asymmetry Reduction in OLDer adults) model suggests that prefrontal activity during cognitive performance becomes less lateralized with advancing age ( Cabeza, 2002 ). In particular, the role of the PFC can be evaluated with TMS or tDCS, as the PFC is easily accessible to modulation with NBS (e.g., Gagnon et al., 2010 , 2011 ).

Besides MTL, PFC, and cortical sites that store contextual features, brain imaging studies suggest that parietal areas also play an important role in episodic memory retrieval ( Wagner et al., 2005 ; Cabeza et al., 2008 ). For instance, according to a recently proposed theory (“COrtical Binding of Relational Activity”, CoBRA), the VPC acts as a binding zone for episodic features and linking these to long-term memory networks ( Shimamura, 2011 ). Both the CoBRA model and the AtoM model (see above) share some similarities, as both suggest that MTL and VPC are linked. Although the role assigned to the VPC differs between the AtoM model (bottom-up processes) and the CoBRA model (integration of event-related activity), they might complement each other. Paired-pulse TMS and the combination of TMS with brain imaging are well suited to examine such notions of corticocortical interactions.

Prospective memory

Prospective memory involves an intention to carry out a psychological or physical act and is related to future-oriented behaviors. In order to realize a goal in the future, it is necessary to retain intentions and activate them at the right time and/or in the appropriate context ( Ellis et al., 1999 ). Depending on the time that passes in between the creation of the intention and the action, and depending on whether the action is triggered externally (context feature) or internally (internal pacemaker), prospective memory involves working and long-term memory processes, as well as attentional processes ( Wittmann, 2009 ). Within this context it has been proposed that, during encoding, prospective memory contents obtain a special status, where they are tagged as not being achieved yet. During the presentation of prospective memory cues, temporal areas are active, possibly representing stimulus-driven attentional processes ( Reynolds et al., 2009 ). The delay period between encoding the intention and the actual act is filled with cognitive activity that prevents active and conscious rehearsal, which differentiates prospective memory from WM or vigilance ( Reynolds et al., 2009 ; Burgess et al., 2011 ). Prospective memory and WM take a special place within the memory domain as they rely strongly on executive processes. However, prospective memory and WM engage different brain areas. Whereas WM demands dorsolateral prefrontal cortex (DLPFC) activity, prospective memory has been associated mainly with activation in the rostral PFC ( Okuda et al., 1998 , 2007 ; Reynolds et al., 2009 ), which is implicated in “future thinking” ( Atance and O’Neill, 2001 ). Such, largely theoretical, considerations derived from careful task analysis and psychological and cognitive model formation can be tested experimentally using NBS.

Working memory

WM refers to the temporary, active maintenance and manipulation of information necessary for complex tasks, while ignoring irrelevant information. It involves the temporary manipulation of external (experienced) or internal (retrieved) stimuli. Like other memory components, it also involves an encoding and retrieval stage. The PFC is an integral component for successful WM performance ( Missonnier et al., 2003 , 2004 ; Jaeggi et al., 2007 ), and NBS offers experimental approaches that were previously limited to animal models.

WM takes a special place within the memory functions, as it is highly dependent on top-down processing and selective attention. Top-down modulation allows us to focus attention on relevant stimuli and ignore irrelevant distractors. This is achieved through an improvement of the signal-to-noise ratio by increasing sensory activity for relevant items and decreasing activity for irrelevant items ( Gazzaley and Nobre, 2012 ). Successful manipulation of information is necessary for encoding as well as the integration of memory functions with other so-called higher cognitive functions associated with conscious processing, such as decision-making, mental imagery, interference control, or language functions. State-dependency experimental designs with NBS ( Silvanto and Pascual-Leone, 2008 ) might allow selective modulation of different items of information and thus shifting of the signal-to-noise ratio. This offers intriguing promises for translational applications of such NBS to populations with WM deficits, such as the elderly or patients with attention-deficit disorders, Parkinson’s disease, or schizophrenia.

UNDERSTANDING THE NEURAL MECHANISMS OF LEARNING AND MEMORY

Learning and memory processes are investigated with a wealth of methods. In the literature we find studies that use brain imaging during memory tasks, analyze the number of remembered items correlated with EEG activity, look at the influence of state changes as captured by various brain imaging and neurophysiological measures, or “borrow patients’ illnesses” to investigate the impact of serendipitous lesions. The application of all these methods has led to valuable information about the neural mechanisms of memory. However, cause–effect relationships are difficult to establish. NBS is uniquely suited to provide this ( Silvanto and Pascual-Leone, 2012 ).

Although TMS and tDCS both promote changes in excitability, they do not rely on the same processes ( Wagner et al., 2007 ; Nitsche et al., 2008 ) and behavioral effects can be different. Neuronavigated TMS can serve to probe the spatio-temporal contribution of certain structures and processes important for learning and memory. It can reveal where and when certain memory processes happen and can shed light on the interplay of multiple processes. On the other hand, the temporal and spatial resolution is lower for tDCS, which is a reason why the utility of tDCS to study spatiotemporal properties of learning and memory is limited. In the following section we concentrate on studies applying TMS as a means to induce so-called “virtual lesions” in the healthy brain ( Pascual-Leone et al., 2000 ). In recent years, research in this field has grown immensely.

Assessing memory functions by induction of virtual lesions in healthy subjects

The first systematic investigation of the contribution of certain brain areas to cognitive functions took place during World War I. Soldiers with circumscribed brain lesions after gunshots provided information about how certain brain regions are associated with cognitive functions ( Lepore, 1994 ). Later, Luria’s work with brain-damaged war veterans contributed strongly to rekindling of the interest in neuropsychology during World War II ( Luria, 1972 ).

Although lesion studies with patients have been widely used since then to investigate learning and memory, they have some disadvantages. Important variables, such as, for example, lesion size, comorbidities, and age, cannot be controlled easily. On the other hand, modern brain imaging methods, such as positron emission tomography (PET) and fMRI, are able to detect regional activation changes with an excellent spatial resolution, and allow for controlled, test–retest experimental designs, but their low temporal resolution does not allow investigation of the organization of distributed memory networks, and they cannot provide information on facilitatory or inhibitory effects or cause–effect relationships. EEG offers a direct measure of brain activity with exquisite temporal resolution, but spatial resolution is in turn limited.

Many of these disadvantages can be overcome when using TMS to induce a “virtual lesion” in an otherwise healthy brain ( Pascual-Leone et al., 1999 ; Walsh and Pascual-Leone, 2003 ). Instead of studying cognitive functions in patients with brain lesions, we can use TMS as a means to induce virtual lesions in healthy subjects and, therefore, reproduce neurobehavioral patterns of patients with brain lesions. TMS is a method that interferes with brain activity and thereby allows probing the chronological contribution of underlying cortical areas. However, it is important to note that our understanding on the neural mechanisms underlying such “virtual lesions” is rather limited, and that a functional disruption is not simply dependent on a mere modification of cortical excitability in the targeted brain area, but appears to involve a complex interplay of inhibitory and excitatory mechanisms, disruption of oscillators, and modification of functional connectivity and synaptic efficacy across distributed neural networks.

TMS has been used in a vast number of studies investigating mechanisms of motor learning and memory ( Bütefisch et al., 2004 ; Censor and Cohen, 2011 ), whereas studies looking at nonmotor memory functions are less numerous. However, recent technical advances allowing the combination of TMS with EEG and fMRI are promising and will allow further exploration of nonmotor memory processes ( Miniussi and Thut, 2010 ; Thut and Pascual-Leone, 2010 ). The combination of methods has, furthermore, the advantage of helping to unravel local and distant effects of brain stimulation and give us insights into functional connectivity.

Most research groups that study WM or STM with NBS methods have focused on the DLPFC or the parietal cortex, believed to be core cortical structures for memory processes. Typically, these studies have used delayed response tasks or n -back tasks to measure STM or WM performance, respectively. A classical example of a delayed match-to-sample task is the Sternberg task ( Sternberg, 1966 ), where the subject is shown a list of numbers or letters and is asked to memorize them. After the delay period, a probe number or letter is shown and the subject has to indicate whether the probe was in the list. Researchers have used several versions of this test using different stimuli and parameters. In “ n -back tasks” a string of visual or auditory stimuli is presented, and subjects have to compare each new stimulus with a stimulus presented n trials back. n -back tasks with n = 1 involve a continuous maintenance and matching of stimuli, whereas n -back tasks with n > 1 furthermore require concurrent engagement of manipulation processes. The reallocation of attention and processing capacity away from mere matching to actual WM processes (by increasing n ) is reflected in decreasing P300 amplitudes ( Watter et al., 2001 ). As these tasks draw on different processes, we will address them in separate sections. Studies using delayed match-to-sample tasks will therefore be summarized under the STM section, whereas studies using the n -back task, or other tasks requiring the online manipulation and integration of stimuli, will be summarized under the WM section. Another major section gives an overview for studies that have investigated encoding, consolidation, and retrieval.

The number of studies that apply TMS and tDCS to address questions regarding the underlying neurobiological structure and modulation of memory functions has grown rapidly in past years. The studies presented in Table 55.1 have applied single-pulse TMS, paired-pulse TMS, repetitive TMS (rTMS), and theta-burst stimulation (TBS). The tasks that were used draw on various processes (attentional, sensory, motor, verbal/nonverbal, spatial/nonspatial, maintenance/manipulation) and stimulation parameters, such as pattern, timing, duration, intensity, and location, vary across studies. It is important to realize that memory tasks vary greatly regarding their specific cognitive demands. In addition, it is important to recognize TMS methodological factors. For example, online stimulation differs from offline stimulation in that underlying brain areas are concomitantly activated through TMS as well as through task performance. This combined activation may affect stimulation outcome. Finally, note that some studies report effects on accuracy, whereas others focus on response times (see Table 55.1 ). It is important to note, though, that the amount of time it takes to recognize an already encountered stimulus or to recall a memorized representation is far less important than the accuracy of this process. Finally, we have to keep in mind that the act of receiving TMS may have an influence on attentional processes that should be carefully controlled for.

Synopsis of peer-reviewed, published studies applying noninvasive brain stimulation in the memory domain

A, C, S, anodal, cathodal, sham; ADAS-Cog, Alzheimer’s Disease Assessment Scale – Cognitive subscale; aMT, active motor threshold; ATL, anterior temporal lobe; BA, Brodmann’s area; BBR, brain-behavior relationship; Cb, cerebellum; CGIC, Clinical Global Impression of Change; CPM, colored progressive matrices; cTBS, continuous theta-burst stimulation; Cz, vertex; DLPFC, dorsolateral prefrontal cortex; DMS, delayed match-to-sample; DMPFC, dorsomedial prefrontal cortex; DR, discrimination rate; EF, executive functions; ERP, event-related potential; Exp., experiment; FEF, frontal eye fields; FL, frontal lobe; fO, frontal operculum; Fz, frontal midline; GDS, Geriatric Depression Scale; HF, high frequency; HVOT, Hooper Visual Organization Test; IADL, Instrumental Activities of Daily Living; IFG, inferior frontal gyrus; IFJ, inferior frontal junction; IPL, inferior parietal lobule; IPS, intraparietal sulcus; ITI, intertrain interval; L, left; LA/RA, left anodal/right anodal; LC/RC, left cathodal/ right cathodal; LF, low frequency; LH, left hemisphere; LOC, lateral occipital cortex; M1, primary motor cortex; MFG, middle frontal gyrus; MMSE, Mini Mental State Examination; MSO, maximum stimulator output; NP, neuropsychological; NPI, neuropsychiatric inventory; OC, occipital cortex; OFC, orbitofrontal cortex; p, pulse; PANAS, positive and negative symptoms scale; PC, parietal cortex; PCG, postcentral gyrus; PD, Parkinson’s disease; PFC, prefrontal cortex; PL, parietal lobule; PM, prospective memory; PMC, premotor cortex; PPC, posterior parietal cortex; ppTMS, paired-pulse transcranial magnetic stimulation; R, right; RBMT, Rivermead Behavioural Memory Test; rCBF, regional cerebral blood flow; ref, reference; RH, right hemisphere; rMT, resting motor threshold; R-pSAC and L-pSAC, right and left parietal somatosensory association cortex; RT, reaction time; rTMS, repetitive transcranial magnetic stimulation; S1, primary somatosensory cortex; SFG, superior frontal gyrus; sign., significant; SMG, supramarginal gyrus; SOA, supraorbital area; sp, single pulse; SPL, superior parietal lobule; stim, stimulation; STM, short-term memory; T, tesla; TL, temporal lobe; TMT, trail making test; TPC, temporoparietal cortex; tRNS, transcranial random noise stimulation; TSOS, transcranial slow oscillation stimulation; VAT, visual attention task; VLPFC, ventrolateral prefrontal cortex; VPFC, ventral prefrontal cortex; VFT, verbal fluency test; VRT, visual recognition task; WAIS, Wechsler Adult Intelligence Scale; WCST, Wisconsin Card Sorting Test; WM, working memory; y, years.

Despite the many differences between studies, the growing literature summarized in Table 55.1 is providing important novel insights in the neurobiology of human learning and memory, and illustrates the power of NBS in this area of cognitive neuroscience.

S hort-term memory

Prefrontal areas undoubtedly play an important role in STM processes. However, one of the questions that NBS studies are helping address relates to the organization of information processing streams. Is processing of STM supported through a domain-specific segregation (spatial, object, verbal processing) or rather through a processing segregation (encoding, maintenance, storage)?

Processing segregation

Most studies examining this question have used a delayed match-to-sample task and applied stimulation during either the delay period or the decision period ( Fig. 55.2 ). High-frequency TMS applied over the parietal cortex during the delay period can improve STM function ( Kessels et al., 2000 ; Kirschen et al., 2006 ; Luber et al., 2007 ; Yamanaka et al., 2010 ), but some studies found it to impair STM ( Koch et al., 2005 ; Postle et al., 2006 ). In either case, the effects seem specific to the delay period, since parietal TMS during the decision phase has not been found to impact STM ( Luber et al., 2007 ; Hamidi et al., 2009 ). The question whether DLPFC also plays a role during the delay phase has not been answered yet. Although some TMS studies support DLPFC participation ( Pascual-Leone and Hallett, 1994 ; Koch et al., 2005 ), others have found no impact when stimulating DLPFC during the delay phase ( Herwig et al., 2003 ; Postle et al., 2006 ; Hamidi et al., 2008 ; Sandrini et al., 2008 ). On the other hand, high-frequency TMS over the DLPFC during the decision period impairs STM functions ( Koch et al., 2005 ; Hamidi et al., 2009 ). Therefore, although further studies are needed, findings suggest a dissociation between parietal and prefrontal areas, playing primary roles in delay and decision phases, respectively. These findings are supportive of the notions of posteroanterior temporal gradient in memory processing: parietal regions coming online first and prefrontal regions contributing to later subprocesses. Chronometric TMS experimental designs enable such notions to be directly tested further.

Schematic summary of findings from studies investigating the impact on short-term memory after stimulation over the left or right prefrontal cortex, parietal areas, or the cerebellum during the delay (green) or the decision period (orange).

Mottaghy et al. (2003a) conducted the first such experiment ( Fig. 55.3 ), albeit focusing on verbal WM. They used single-pulse TMS to explore the temporal dynamics of left and right inferior parietal and DLPFC involvement in verbal WM in six healthy volunteers. TMS was applied at 10 different time points 140–500 ms into the delay period of a 2-back verbal WM task. Precise and consistent targeting of a given cortical brain region was assured by using frameless stereotactic neuronavigation. A choice reaction task was used as a control task. Interference with task accuracy was induced by TMS earlier in the parietal cortex than in the PFC, and earlier over the right than the left hemisphere. This suggests a propagation of information flow from posterior to anterior cortical sites, converging in the left PFC. Significant interference with reaction time was observed after 180 ms with left PFC stimulation. These effects were not observed in the control task, underlining the task specificity of our results. Hamidi and colleagues (2009) also examined the roles of right and left DLPFC in recall and recognition. They found that right DLPFC stimulation impaired accuracy in delayed recall, while enhancing accuracy in delayed recognition. On the other hand, left DLPFC stimulation impaired delayed recognition. Therefore, it seems clear that TMS, in repetitive and chronometric single-pulse experimental designs, can provide valuable insights into the functional segregation of core subprocesses of STM.

Accuracy in the 2-back task as a function of the time of transcranial magnetic stimulation (TMS). TMS interference peaked at 180 ms at the right inferior parietal cortex, at 220 ms at the left inferior parietal cortex and right middle frontal gyrus (MFG), and at 260 ms at the left MFG (all p < 0.05). This study illustrates the chronometry of causal contributions of different brain regions to memory processing. (Modified from Mottaghy et al., 2003a , by permission of the authors.)

Domain-specific segregation

Mottaghy et al. (2002b) , in another pioneering study ( Fig. 55.4 ) used TMS to show that functional and modality-specific segregation need not be mutually exclusive. They applied low-frequency rTMS to explore the functional organization of STM by selectively disrupting the left dorsomedial PFC (DMPFC), DLPFC, or ventral PFC (VPFC). They applied a 10-min 1-Hz rTMS train before assessing spatial or nonspatial (face recognition) delayed-response performance. Spatial task performance was impaired after rTMS to DMPFC, whereas nonspatial task performance was impaired after rTMS to VPFC. Disruption of the DLPFC affected the performance in both tasks. This finding reveals a task-related segregation of processing streams along prefrontal structures. More recent studies have confirmed the utility of TMS to offer empirical support for modality-specific segregation. For example, Soto et al. (2012) combined evidence from fMRI and rTMS to demonstrate that verbal and nonverbal memories interact with attention functions independently: whereas rTMS to the superior frontal gyrus disrupted STM effects from colored shapes, rTMS to the lateral occipital cortex disrupted effects from written words. Finally, Morgan and colleagues (2013) used TMS to reveal the neural substrates for integration of segregated features of STM processes. They investigated STM for colors, orientations, and combinations of these, and found that continuous TBS (cTBS) over the right parietal cortex or left inferior frontal gyrus selectively impaired STM for combinations but not for single features. Therefore, functional coupling between frontal and parietal areas appears to be critical to bind modality-specific segregated processes.

Study exploring the segregation of memory processes in prefrontal cortex. Two alternative models were proposed based on the data. ( A ) There might be two different, nonoverlapping, functionally segregated regions within the prefrontal cortex (PFC) that are domain-specific (S, spatial domain; F, face domain). Repetitive transcranial magnetic stimulation (TMS) over the dorsomedial PFC (DMPFC) interferes only with the processing of the spatial information. Dorsolateral PFC (DLPFC) stimulation might have induced overlapping interference of two adjacent domain-specific areas, whereas the ventral PFC (VPFC) led only to interference with the processing of the face stimuli. ( B ) The DMPFC and the VPFC interference effects can be explained in the same manner as in proposal ( A ); however, the performance deterioration over the DLPFC in this model might be explained by the interference with information processing of a common module (C) that is employed during both types of stimulus. (Modified from Mottaghy et al., 2002b , by permission of the authors.)

Frontoparietal binding

Frontoparietal interactions in memory formation and maintenance appear to be dynamic and NBS studies – particularly studies combining TMS with MRI or EEG – help gaining critical insights in this regard.

In the motor domain, frontoparietal interactions seem to be particularly important in the early phase of learning, as has been shown in a recent study combining TMS and EEG ( Karabanov et al., 2012 ). In the nonmotor domain, a recent TMS–fMRI study ( Feredoes et al., 2011 ) found that DLPFC contribution to maintenance of stimuli in STM is highly dynamic depending on the presence or absence of distractors. In the presence of distractors, DLPFC changes its communication with posterior regions to support maintenance. These results are supported by tDCS studies that assign the DLPFC an important role in STM in the presence of distractors ( Gladwin et al., 2012 ; Meiron and Lavidor, 2013 ). Zanto and colleagues (2011) combined EEG with 1-Hz rTMS to the right inferior frontal junction to investigate the contribution of the prefrontal cortex in top-down modulation of visual processing and STM in a delayed-match-to-sample task. They found that EEG patterns from posterior electrodes, which are associated with the distinction of task-relevant and -irrelevant stimuli during early encoding, were diminished after TMS, which again predicted a subsequent decrease in STM accuracy. Subjects with stronger frontoposterior functional connectivity furthermore showed greater disruption. Higo and colleagues (2011) combined offline TMS over the frontal junction with subsequent fMRI to explore the same question. They also observed a TMS-induced decrease of effects in posterior regions depending on task relevance/irrelevance. The inferior frontal junction may therefore control the causal connection between early attentional processes and subsequent STM performance, and may regulate the level of activity of representations in posterior brain areas depending on their relevance/irrelevance for response selection.

It could be hypothesized that the interaction between frontal and posterior areas during the delay period secures the maintenance of information, especially if this information needs to be protected from distracting information. These processes may be related to the regulation and reactivation of patterns that were active during encoding. Accordingly, frontal areas might recruit neuronal assemblies and regulate their activity in posterior areas in order to protect and actively maintain information. Such activations may be most prominent at the beginning of the delay period and decrease gradually.

Other brain regions involved in STM

Frontal and parietal areas are undoubtedly the most explored areas in STM. Although it has been debated in the literature, there is some evidence that the cerebellum may also be involved in STM. When Desmond and colleagues (2005) applied single-pulse TMS (at 120% resting motor threshold) over the right superior cerebellum at the beginning of the delay phase, they found an increased reaction time but no change in accuracy for correct trials in the Sternberg task. This is in agreement with a tDCS study that probed the cerebellum and found an abolishment of practice-dependent improvements in reaction time after anodal as well as cathodal tDCS in a Sternberg task ( Ferrucci et al., 2008 ).

Last, but not least, cortical areas implied in sensory processing are also believed to be involved in STM of sensory information, which may be guided through attentional processes. A number of TMS studies have shown a role of visual cortex with visual STM and WM (see review by Postle et al., 2006 ). A few studies have furthermore investigated the tactile domain. Application of TMS to the visual cortex during the delay phase of STM tasks results in a decrease of accuracy in the targeted visual field for high memory loads ( Van de Ven et al., 2012 ) or a decrease in memory scanning rates ( Beckers and Hömberg, 1991 ). The effect of TMS was furthermore shown to be different if applied at the beginning (inhibitory) or at the end (facilitatory) of the delay period in both a visual STM task and imagery ( Cattaneo et al., 2009 ). This is an elegant application of state-dependency TMS experimental designs ( Silvanto and Pascual-Leone, 2008 ). Although neurons implicated in encoding are highly active at the onset of the retention period, TMS might preferentially activate neurons not involved in encoding, thereby reducing the signal-to-noise ratio of the memory trace, and impair behavior.

In the tactile domain, a TMS study using single-pulse stimulation over the middle frontal gyrus (MFG) during the early maintenance period led to a decrease in reaction time in a tactile STM task, even in the presence of a distracting stimulus ( Hannula et al., 2010 ). In a follow-up study, the same group investigated whether this improvement only occurs when the interference is tactile, or whether MFG creates a more general top-down suppression ( Savolainen et al., 2011 ). Their results showed that TMS did not lead to facilitation when a visual interference was presented, but only when the interfering stimulus was also tactile.

These and other findings (e.g., Silvanto and Cattaneo, 2010 ) suggest that sensory brain areas involved in early, modality-specific, processing of perceptual stimuli contribute to the formation and maintenance of STM representations through an interaction with the attentional system. In this context, TMS can help elaborate the chronology of memory processes and contributions of state-dependent processes.

W orking memory

WM has been investigated with NBS in a growing number of studies. As for STM, most of these studies have explored the roles of DLPFC and parietal areas, trying to find an answer to the question of whether information is separately processed with regard to domain or functional subprocess ( Fig. 55.5 ). In addition, some studies have examined the question of whether the same areas that participate in STM are also active in WM tasks.

Schematic summary of findings from studies investigating the impact on verbal (red) or nonverbal (blue) working memory (WM) after stimulation over the left or right prefrontal cortex (PFC), parietal areas, or cerebellum.

Verbal and nonverbal WM in DLPFC

Again building on pioneering work from Mottaghy et al. (2000) , most researchers have found an impairment of verbal WM after stimulation of the left DLPFC ( Mull and Seyal, 2001 ; Mottaghy et al., 2000 , 2003a ; Postle et al., 2006 ; Osaka et al., 2007 ) and after stimulation of the right DLPFC ( Mottaghy et al., 2003a ; Postle et al., 2006 ; Sandrini et al., 2008 ). However, some studies failed to find such effects ( Mull and Seyal, 2001 ; Rami et al., 2003 ; Imm et al., 2008 ; Sandrini et al., 2008 ).

The role of DLPFC in nonverbal WM has been studied much less ( Oliveri et al., 2001 ; Imm et al., 2008 ; Sandrini et al., 2008 ). Sandrini and colleagues (2008) tried to clarify domain- and process-specific contributions of the DLPFC. They presented physically identical stimuli (letters in different spatial locations) in a 1-back task (STM) and a 2-back task (WM). Furthermore, they presented the 2-back task with stimuli of both or just one domain. A short train of 10-Hz rTMS was applied at the end of the delay period between stimuli. They found interference only during the 2-back task, and only when stimuli from both domains were presented. Interestingly, performance in the letter task was impaired after rTMS over the right DLPFC, whereas performance in the location task was impaired after rTMS over the left DLPFC. These results were interpreted as an interference effect on control mechanisms (central executive) in the sense of the suppression of task-irrelevant information. The same hypothesis has been put forward with regard to the protection of memory contents in STM ( Feredoes et al., 2011 ; Higo et al., 2011 ; Zanto et al., 2011 ), according to which an interaction between frontal and posterior areas during the delay period secures the maintenance of information, especially in the presence of distractors.

Further experiments have aimed at dissecting the role of DLPFC in WM in order to find out whether domain- or process-specific models should be favored, and others have examined the role of interactions between DLPFC and other brain areas. Combination of TMS with brain imaging has proven quite valuable in this context. Mottaghy and colleagues (2000) found that performance in a verbal WM (2-back) task was significantly diminished after rTMS (30-second train of 4-Hz rTMS) to the left but also the right DLPFC (F3/F4). Importantly, by combining TMS with PET, they showed that TMS-altered performance in the WM task was associated with a reduction in regional cerebral blood flow (rCBF) at the stimulation site and in distant areas as assessed with PET. In an elegant follow-up TMS–PET study, the same authors ( Mottaghy et al., 2003b ) showed that at baseline (in the absence of TMS) there was a negative correlation between rCBF in the left (but not the right) DLPFC and WM task performance. Application of rTMS to the left or the right DLPFC could disrupt WM performance, but appeared to do so on the basis of different distributed impact on a bihemispheric network of frontal and parietal regions: whereas rTMS over the left DLPFC led to changes in rCBF in the directly targeted left DLPFC and the contralateral right PFC, rTMS over the right DLPFC led to more distributed changes involving not only bihemispheric prefrontal, but also parietal areas ( Fig. 55.6B ). Regardless of the differential network impact of the right or left stimulation, the behavioral consequences of rTMS were always related to the impact onto left DLPFC rCBF. This study highlights a number of important findings of relevance for future studies on NBS in memory and learning. First, it shows that rTMS to different nodes of a given brain network can exert differential impact onto said brain network. More recently, Eldaief et al. (2011) have expanded on this line of inquiry combining resting-state fMRI with TMS to examine brain network dynamics. Second, the study shows that network dynamics are modified by behavioral engagement. In other words, it might be possible to learn about mechanisms of memory and learning by examining how the impact of TMS onto a given brain network is modulated by the behavioral state. Finally, the study illustrates that brain stimulation can affect behavior by disrupting a computation in the targeted brain region (as in the case of left DLPFC rTMS) or by disrupting function of a brain regions reached via trans-synaptic network impact (as in the case of rTMS to the right DLPFC altering left DLPFC via interhemispheric connections). This later finding is important in the interpretation of brain stimulation results in general, and illustrates the power of studies integrating brain stimulation with neuroimaging in exploring causal relations between brain activity and behavior ( Fig. 55.6A ). In a later study, Mottaghy and colleagues (2003a) applied single-pulse TMS at different time points after stimulus presentation to probe the temporal dynamics of parietal and prefrontal contributions in verbal WM. With this approach they were able to add chronometric information to their prior findings. They showed that single-pulse TMS could interfere with task accuracy earlier in the parietal than in the PFC, and earlier over the right than left hemisphere. This indicates an information flow from posterior to anterior converging in the left PFC. These series of studies reveal that both hemispheres contribute to WM, but that the computation performed by the left PFC is critical in verbal WM.

Transcranial magnetic stimulation–positron emission tomography (TMS–PET) study of the neurobiological substrates of working memory. ( A ) The impact of TMS on behavior relies on activity changes in local and distributed brain networks. The combination of TMS with brain imaging techniques, such as EEG, fMRI, PET, and EMG, allows us to detect correlations between these activity changes and behavior. Moreover, it allows study of the impact of state dependency on stimulation outcome. ( B ) Positive (green) and negative (red) correlation between regional cerebral blood flow (rCBF) and performance in the 2-back working memory (WM) task (1) without application of repetitive TMS (rTMS), (2) with rTMS delivered over the left middle frontal gyrus (MFG), and (3) with rTMS applied over the right MFG. While rTMS over the left MFG has a local impact, which is correlated with behavior, rTMS over the right MFG has an impact on a distributed network, including homologous areas. Importantly, also in the case of stimulation over the right MFG, activity changes in the left MFG but not right MFG are correlated with behavioral output. This key finding shows that the effect of TMS can be achieved by a direct effect on underlying areas, but also through trans-synaptic effects (e.g., in homologous areas). The combination of TMS with imaging techniques is crucial in order to identify neural substrates associated with behavioral output. (Modified from Mottaghy et al., 2003b , by permission of the authors.)

Interestingly, involvement of DLPFC, regardless of stimulus modality, has been shown in an often-cited study using bilateral single-pulse stimulation during a 2-back task ( Oliveri et al., 2001 ). Early temporal stimulation (300 ms) increased reaction time for object-related WM, whereas early parietal stimulation and late stimulation (600 ms) over the superior frontal gyrus increased reaction time for spatial WM. However, late DLPFC stimulation interfered with both tasks and not only with RT, but also with accuracy. These results relate to the discrimination of a dorsal (“where”) and ventral (“what”) pathway and again information flow from parietotemporal to frontal areas. They indicate that there might not only exist a bilateral involvement of the DLPFC in verbal WM, but that DLPFC might be active irrespective of stimulus material, unlike other prefrontal regions that may be segregated (see e.g., Mottaghy et al., 2002b ). Segregation in posterior areas seems to be easier to pinpoint, and is concordant with the view that both hemispheres are implicated in spatial and object WM tasks ( Smith and Jonides, 1997 ). The research that has been done up to date generally points into the direction of favoring a process-specific model for DLPFC, whereas other areas of the prefrontal or parietal cortex may be modality-based. Possibly, WM operations relying on DLPFC, such as selective attention and other executive processes (e.g., the inhibition of task-irrelevant stimuli), are independent of modality ( Smith and Jonides, 1999 ) and play a role in both STM and WM. The combination of fMRI and EEG with TMS may help us to disentangle further the interactions of DLPFC and other prefrontal and parietal areas to WM functions.

P rospective memory

Prospective memory is tightly connected with other memory subcomponents (see Fig. 55.1 ), which makes it difficult to single out its processes. Perhaps this challenge accounts for the fact that few studies to date have explored prospective memory using NBS. One study ( Basso et al., 2010 ) investigated whether verbal WM and prospective memory are based on common or separate processes. In a first experiment participants had to accomplish tasks with low, medium, or high WM load. In the prospective condition, subjects had to react whenever a specific word appeared. In a second experiment the prospective conditions included 1 or 3 prospective words. A higher prospective memory demand interfered with the WM task only at higher loads, whereas WM activity did not affect prospective memory performance. If both processes were part of the same system one might expect a trade-off. In a third experiment single-pulse TMS was applied to the left and right DLPFC in order to test the notion that WM and prospective memory rely on distinct systems. TMS to the DLPFC increased error rates in the prospective memory task, whereas the effect on the WM task was only marginal. No difference between hemispheres was detected. The authors concluded that WM and prospective memory may not be based on the same memory system. However, it is hard to rule out that prospective memory may require resources (including in part WM resources) and may thus be easier to disrupt with TMS. More complex TMS designs, such as input–output designs with TMS applied at various intensity and timings, seem necessary to explore this issue further.

Costa and coworkers (2011) investigated the effects of cTBS (80% active motor threshold) on prospective memory. Stimulation over left Brodmann area (BA) 10 (frontal pole) resulted in impaired accuracy as compared with stimulation over Cz. In a second experiment they did not find a significant difference after cTBS over left BA46 (DLPFC) and Cz. They concluded that the left BA10 is important for prospective memory processes. This is in accordance with a neuroimaging study ( Koechlin et al., 1999 ) that tried to dissociate the roles of frontopolar and DLPF cortices in prospective memory. Costa and colleagues employed a fairly novel TMS paradigm (cTBS) and tackled a complicated memory construct (prospective memory). However, this important, innovative study also illustrates one important challenge for all studies using NBS in memory: it is ultimately critical to have separate empirical demonstration of the impact of brain stimulation on brain function, and on behavior. In fact, ideally, one would want to apply TMS, measure the behavioral impact and the impact on brain physiology, and then correlate one with the other (see Fig. 55.6A ). Costa and coworkers (like most investigators using TMS or tDCS in studies of memory) placed the TMS coil over the scalp overlaying the brain regions they wanted to target (frontal pole or DLPFC). They then assumed that the TMS impact on brain activity would be maximal in the underlying cortex. They assessed the impact of TMS onto prospective memory and assumed that said impact must reflect the consequence of TMS-induced change in activity in the targeted brain region. There is a risk of circular logic in this approach: “If TMS over a given region has a predicted impact onto a given memory process, then I have shown that said brain region was affected by TMS and that it plays said role in memory.” Obviously, independent empirical demonstration of these two steps would be important and the use of NBS in studies of memory, or studies of cognitive functions in general, should aim to achieve such experimental discrimination.

E ncoding, consolidation, retrieval

Some studies have applied rTMS during the encoding phase and support the critical role of the PFC in such memory processes. Stimulation of the left DLPFC during the encoding phase has been found to affect both verbal ( Grafman et al., 1994 ; Rami et al., 2003 ; Sandrini et al., 2003 ; Flöel et al., 2004 ; Skrdlantová et al., 2005 ; Blanchet et al., 2010 ; Gagnon et al., 2010 , 2011 ) and nonverbal ( Rossi et al., 2001 , 2004 ; Blanchet et al., 2010 ; Gagnon et al., 2010 , 2011 ) memory. However, a few studies have reported an impact on memory functions after stimulating right frontal areas during the encoding phase of verbal ( Grafman et al., 1994 ; Sandrini et al., 2003 ; Kahn et al., 2005 ; Blanchet et al., 2010 ; Machizawa et al., 2010 ) or nonverbal ( Epstein et al., 2002 ; Flöel et al., 2004 ; Blanchet et al., 2010 ) memory functions. Some investigators did not find any effects after stimulating right frontal cortex ( Rami et al., 2003 ; Köhler et al., 2004 ). No effects have been found after stimulating parietal ( Köhler et al., 2004 ; Rossi et al., 2006 ) or occipital cortex ( Grafman et al., 1994 ). Only one study reported impairment after stimulating the temporal cortex ( Grafman et al., 1994 ).

Fewer studies have applied TMS during the retrieval phase of memories. Stimulation of the right DLPFC during the retrieval phase appears to be associated with an impact on both verbal ( Sandrini et al., 2003 ; Gagnon et al., 2010 , 2011 ) and nonverbal ( Rossi et al., 2001 , 2004 ; Gagnon et al., 2010 , 2011 ) memory. No studies have reported an effect after stimulation of the left hemisphere during the retrieval phase.

Several studies have used NBS to reveal the important role of the ventrolateral PFC (VLPFC) in the formation of long-term memory ( Grafman et al., 1994 ; Flöel et al., 2004 ; Köhler et al., 2004 ; Machizawa et al., 2010 ), and it has been suggested that VLPFC may be material-specific whereas DLPFC is not. Further studies are needed to shed light on these mechanisms.

Recent studies by Gagnon and colleagues explicitly addressed the assumptions of the HERA model ( Blanchet et al., 2010 ; Gagnon et al., 2010 , 2011 ) and tried to shed light on the contribution of left and right DLPFC in encoding and retrieval of verbal as well as nonverbal information. These are particularly important studies as they illustrate the value of TMS in the systematic testing of key aspects of a well formulated cognitive conceptual model. It is this type of experimental approach that can fully leverage the advantages of TMS in studies of memory and learning. In a first study, Gagnon et al. (2010) applied paired-pulse TMS (interstimulus interval (ISI) 3 ms) over the left or right DLPFC during encoding or retrieval of verbal (words) and nonverbal stimuli (random shapes). They found that left and right DLPFC play different roles in encoding and retrieval irrespective of stimulus type: stimulation of the left DLPFC during encoding resulted in discrimination deficits, whereas stimulation of the right DLFPC during retrieval resulted in a reduced hit and disrimination rate. In a follow-up study they applied paired-pulse TMS with a longer ISI (15 ms) to promote facilitation (rather than cortical suppression) to the left and right DLPFC during encoding or retrieval of verbal (words) and nonverbal stimuli (random shapes) ( Gagnon et al., 2011 ). They found a facilitation of reaction times during encoding (left DLPFC) and retrieval (right DLPFC) regardless of the type of material presented. These results are consistent with other TMS studies ( Rossi et al., 2001 , 2006 ; Rami et al., 2003 ) and provide experimental support for the HERA model, which proposes that the left PFC is more involved in semantic retrieval and episodic encoding than the right PFC, whereas the right PFC is involved in episodic retrieval ( Tulving et al., 1994 ). This hemispheric asymmetry seems to uphold for both verbal and nonverbal material ( Haxby et al., 2000 ; Blanchet et al., 2010 ).

USING NONINVASIVE BRAIN STIMULATION AS A DIAGNOSTIC TOOL

In addition to uses in cognitive neuroscience, it is worth considering the potential utility of NBS in clinical neuroscience as a diagnostic tool. Diagnostic applications of NBS are appealing as they are noninvasive and can be applied safely to various patient populations across the lifespan, if appropriate precautions are taken and guidelines are followed ( Rossi et al., 2009 ). TMS has an excellent temporal resolution and its spatial resolution is superior to tDCS, which are important advantages in diagnostic applications and make TMS a superior tool to probe brain reactivity and brain connectivity.

To date, TMS has not been established as a diagnostic tool. However, if we define carefully the areas of need in specific patient populations, we may be able to complement currently used test measures, which rely mainly on behavioral assessments ( Rost et al., 2008 ; Sigurdardottir et al., 2009 ; Gialanella, 2011 ; Wagle et al., 2011 ).

As for motor dysfunctions, nonmotor memory functions could be characterized by changes in the excitation/inhibition (E/I) balance and cortical plasticity in specific brain areas, which could be assessed with TMS–EEG measures ( Thut and Pascual-Leone, 2010 ). Changes of such neurophysiological measures over the time-course of cognitive rehabilitation, during normal and pathological aging, or in response to treatment of disease could help us establishing neurophysiological biomarkers indicative of functional improvements. Such measures could not only be helpful to differentiate across pathological entities, but may also disentangle underlying causes of memory dysfunctions on an individual level. Finally, this information could help develop novel and improve existing interventions in order to improve memory functions.

In the memory domain there are several questions worth exploring with TMS as a diagnostic tool: (1) What is the pathogenesis of present memory problems? (2) Who is at risk of developing memory problems and what kind of memory problems? (3) Who is likely to benefit from a given behavioral/physiologic/pharmacological intervention?

Identify the pathogenesis of memory problems

Depending on the etiology, the pathogenesis of an individual patient’s memory problem can be vastly different and be affected by many factors including age, environmental, and genetic predispositions. Regardless of etiology, though, one can also aim to identify the proximal, neural dysfunction that accounts for a given memory deficit. TMS can be applied to gain insights at both these levels of inquiry.

Single- and paired-pulse TMS measures may reveal changes in connectivity or altered network dynamics and link those to specific memory functions. Advanced combined technologies such as TMS–EEG or TMS–MRI allow us to utilize TMS-induced cortical evoked potentials or TMS-induced blood oxygen level-dependent (BOLD) fMRI changes as neural measures of brain activity in specific brain regions or networks to relate to behavioral memory measures.

rTMS paradigms, for example intermittent and continuous TBS stimulation (iTBS and cTBS), can be used to obtain indices of cortical plasticity that appear related to long-term potentiation and depression (LTP and LTD)- like induction of synaptic plasticity. Such paradigms can be used to evaluate cortical plasticity in neural structures thought to support memory processes and may allow us to draw conclusions regarding the pathogenesis of a memory problem. For example, a cortical lesion within a widespread memory network could not only have a direct impact on memory functions caused by this particular lesion but could also lead to indirect deficits due to disconnection of the lesioned area with another memory hub. TMS measures could inform us about acute processes as well as adaptive or maladaptive changes characteristic of chronic processes that lead to memory dysfunctions ( Pascual-Leone et al., 2011 ).

Identify risk for developing memory problems

Another major area of interest lies in the possible use of TMS as a physiological biomarker, which could indicate the individual risk of developing memory dysfunctions with age and predict what kind of memory problems could be expected in certain populations. Cognitive decline including memory functions presents a critical hallmark of aging ( Morrison and Baxter, 2012 ). Early changes in neuroplasticity and neurophysiological circuits indicated by TMS measures, such as short-latency afferent inhibition (SAI), could constitute biomarkers for the development of neurodegenerative disorders ( Freitas et al., 2011b ). Risk identification with this approach requires the integration of numerous factors associated with causal and formal pathomechanisms, including age-related changes, but also, for example, changes related to systemic diseases, such as diabetes mellitus, that may indirectly have an impact on brain physiology and plasticity. TMS could be a valuable tool to identify these factors and consequently help guide and implement early interventions in populations at risk.

Another approach is using TMS measures to identify risks related to interventions that could result in brain lesions or dysfunctions. For example, consider neurosurgical interventions: presurgical detailed knowledge about functional contributions of brain areas to be resected can critically inform surgical approaches and minimize the risk. In this context, the Wada test can be used to determine hemispheric language dominance prior to brain surgery ( Wada and Rasmussen, 1960 ). However, this test has a non-negligible risk of complications and discomfort for the patient and does not allow precise functional localization. Neuronavigated TMS can provide detailed information regarding functional anatomy of the targeted brain area and is potentially valuable for presurgical planning not only in regard to language dominance ( Pascual-Leone et al., 1991 ; Devlin and Watkins, 2007 ), but also in regard to memory ( Grafman et al., 1994 ). Such noninvasive neuronavigated TMS cortical mapping appears to correlate well with direct cortical stimulation (DCS) results and seems to be more precise than fMRI, which is the most widely used technique today ( Krieg et al., 2012 ). As DCS is limited to intraoperative use, presurgical TMS might also save operation time by guiding intraoperative DCS.

Predicting benefit from a given intervention/medication

Cognitive rehabilitation consists in assessment-based therapeutic interventions aiming to reduce disability and promote functional recovery. Functional changes are achieved through various intervention methods targeting restitution, compensation, and adaptation ( Cicerone et al., 2000 ). But how can we determine whether a given therapeutic intervention will have a beneficial effect for an individual patient?

TMS measures may be used not only to track but also to predict intervention-related neuroplastic changes within memory networks. Moreover, TMS measures can inform us about the functionality of specific neurophysiological circuits implicated in memory functions and may be indicative of how well an individual will profit from a given pharmacological intervention. For instance, acetylcholine (ACh) is a neurotransmitter that plays a crucial role in synaptic plasticity and memory functions, and ACh imbalances have been associated with memory deficits in patients with Alzheimer’s disease (AD) ( Davies and Maloney, 1976 ; Coyle et al., 1983 ). Deficits in cholinergic circuits can be counteracted with pharmacological interventions involving acetylcholine esterase (AChE) inhibitors. SAI is a TMS measure that is indicative of cholinergic circuits in the motor cortex ( Di Lazzaro et al., 2000 ) and is altered in patients with AD (for a review see Freitas et al., 2011a ). SAI may even be useful to differentiate dementia subtypes ( Di Lazzaro et al., 2006 , 2008 ) and may be used as an indicator of who will profit from AChE inhibitors. Short-latency intracortical inhibition (SICI) and the cortical silent period (cSP) are thought to reflect the excitability of inhibitory γ-aminobutyric acid (GABA)ergic circuits ( Hallett, 2000 ) and were also found to be abnormal in patients with AD. However, the relationship of these TMS measures with specific memory dysfunctions is less clear ( Freitas et al., 2011a ). Notably, studies up to date have relied on TMS measures from the motor cortex. However, the combination of TMS with EEG may enable us to find more precise TMS biomarkers by exploring neurophysiological changes outside the motor cortex.

MODULATING LEARNING AND MEMORY

The interest in the augmentation of cognitive functions reaches far back into the history of modern humanity. The use of memory techniques, for instance in order to improve rhetorical skills, was already promoted by Marcus Tullius Cicero (“De Oratore”, Book II, 55 bc ). One of these methods, the “Cicero Memory Method” (Method of loci), a simple memory enhancement method that uses visualization to structure information, is still in use today. The pursuit of cognitive augmentation has since led researchers to take advantage of technical developments in order to achieve a better outcome. In the past decade, scientists have therefore started investigating the impact of various NBS techniques on memory functions.

Learning is a prerequisite for the formation of memory traces and is thought to be dependent on synaptic plasticity mediated by LTP and LTD, which also represent key mechanisms in the effects of NBS on brain functions. This has not only rendered NBS valuable for the investigation of neuroplastic processes associated with learning and memory but also promotes it as a valuable tool to enhance memory functions.

Although TMS is used mostly for diagnostic purposes and the investigation of brain structures contributing to specific functions, tDCS is more often applied to enhance brain functions.

Healthy subjects

In the past decade, researchers have begun examining the effects of WM training on neural correlates and concomitant performance ( Jaeggi et al., 2008 ). These studies have shown that not only can WM capacity be increased via constructive training but also that said training increases the density of cortical D1 dopamine receptors in prefrontal regions ( McNab et al., 2009 ). The neurobiological substrate of WM is an ongoing topic of research; however, prefrontal regions are believed to be critically involved. Consistent with such notions, studies exploring the potential for NBS to enhance WM have focused on the prefrontal cortex, generally the DLPFC, and the majority have used verbal WM tasks. In most studies subjects were asked to practice STM or WM tasks concurrently to tDCS, and their WM abilities were assessed either during or afterwards.

Compared with sham stimulation, tDCS with the anode over the left DLPFC (and the cathode right supraorbitally) has been repeatedly reported to enhance WM in healthy subjects ( Fregni et al., 2005 ; Ohn et al., 2008 ; Mulquiney et al., 2011 ; Teo et al., 2011 ; Zaehle et al., 2011 ). Some researchers have suggested that increasing stimulation intensity ( Teo et al., 2011 ) or duration ( Ohn et al., 2008 ) might lead to more robust effects. Only one study has reported no memory improvement following tDCS with the anode over the left DLPFC ( Mylius et al., 2012 ), and one study reported improvement in STM but not in WM ( Andrews et al., 2011 ). The only study applying tDCS with the anode over the right DLPFC showed no WM effect ( Mylius et al., 2012 ). On the other hand, tDCS with the cathode over the left DLPFC (and the anode right supraorbitally) yielded diverse results in different studies, ranging from memory benefits ( Mylius et al., 2012 ), to no effects ( Fregni et al., 2005 ), and even negative effects ( Zaehle et al., 2011 ). The study by Zaehle et al. (2011) is of particular interest as the authors reported that the negative effects of tDCS with the cathode over the left DLPFC were associated with decreased electroencephalographic power in theta and alpha bands over posterior (parietal) regions. On the other hand, the authors found that improved WM following tDCS with the anode over the left DLPFC was associated with increased power in alpha and theta EEG bands over parietal regions. This study illustrates the potential of studies combining behavioral and neurophysiological outcome measures, and suggests the critical role of corticocortical interactions in memory enhancement. It has been proposed that a more distributed network may subserve WM functions with the posterior parietal cortex (PPC) playing an important role ( Mottaghy et al., 2002a ; Collette et al., 2006 ). Stimulation might disrupt activity in a given cortical region and thus release activity in a distant connected node, resulting in paradoxical facilitation ( Najib and Pascual-Leone, 2011 ). The specific nature of the stimulation seems important, although, for example, random noise stimulation over the left DLPFC showed no effects ( Mulquiney et al., 2011 ).

In order to explore further the role of parietal structures in WM, Sandrini and colleagues (2012) applied bilateral stimulation over the PPC during a 1-back (STM) or a 2-back (WM) task. They found a double dissociation, with STM being impaired after left-anodal/right-cathodal and WM being impaired after left-cathodal/right-anodal stimulation. They concluded that this dissociation might be due to differential processing strategies in STM and WM. However, the effects might have been mediated by impact on attentional (rather than memory) processes given the fact that only response time, and not accuracy, was affected. Future studies will need to investigate further the contribution of parietal areas and their interaction with prefrontal areas to WM enhancement.

Further studies could examine the duration of effects, the likely synergistic effect of cognitive training with tDCS, or the applicability of tDCS or other NBS methods to enhance WM across the age span, from children to elderly. However, all such studies need carefully to weigh risk–benefit considerations, and should be informed by a thoughtful discussion of the ethical connotations of such enhancement approaches ( Rossi et al., 2009 ; Hamilton et al, 2011 ; Horvath et al., 2011 ).

Whether NBS can enhance STM in normal subjects is less clear. Studies show less consistent results. This could in part be due to the fact that basic STM tasks are easy for healthy subjects, which leads to ceiling effects. More recent studies have applied adapted tasks, which, however, makes it difficult to compare across studies. Most studies, similar to the literature on WM, have targeted the DLPFC. Two recent studies reported beneficial effects of tDCS with the anode over the DLPFC for an STM task with additional distractors ( Gladwin et al., 2012 ; Meiron and Lavidor, 2013 ). One study found a gender-dependent improvement in accuracy, with male subjects profiting more from left DLPFC stimulation and female subjects profiting more from right DLPFC stimulation, but only if distractor loads were high ( Meiron and Lavidor, 2013 ). The other study used a modified Sternberg task, which introduced additional distractor stimuli during the delay period ( Gladwin et al., 2012 ). These workers found significant reaction time improvements after stimulation of the left DLPFC. Compared with these studies, Marshall et al. (2005) applied tDCS with either two anodes or two cathodes over DLPFC, with the reference electrodes positioned over the mastoids, and found deleterious effects of STM. This may indicate that the introduction of distractors to an STM task changes underlying neurobiological processes and enables enhancement effects. Improvements after tDCS may be due to either improved selective attention or more successful inhibition of distracting information. Indeed, a recent TMS study has shown that the role of the DLPFC in STM tasks seems to be dependent on the presence of distractors. The stronger the distraction, the more prominent the frontoparietal interactions become, in order to protect relevant memory representations ( Feredoes et al., 2011 ).

Studies in which investigators stimulated parietal areas have yielded partly opposing results. This is true of studies using tDCS and those employing TMS. Regarding TMS experiments, some show worsened STM ( Koch et al., 2005 ; Postle et al, 2006 ), while the other report improved STM ( Hamidi et al., 2008 ; Yamanaka et al., 2010 ) after high-frequency parietal stimulation during the delay period. As for tDCS experiments, Berryhill et al. (2010) found impairment in recognition, but not free recall, after tDCS with the cathode over the right parietal cortex (and the anode over the left cheek), whereas Heimrath and coworkers (2012) , positioning the cathode over the right parietal cortex (and the anode over the contralateral homologous area), found an improved capacity in a delayed match-to-sample task after tDCS when stimuli were presented in the left visual hemifield (STM for stimuli presented in the left hemifield decreased). Interestingly, Heimrath et al. used concurrent tDCS and EEG, and found a decrease in oscillatory power in the alpha band after cathodal stimulation. As alpha activity is assumed to reflect inhibition of distractors ( Klimesch, 1999 ), the authors suggested that this measure might indicate memory performance. This study again illustrates the potential of experiments combining behavioral and neurophysiological outcome measures with NBS.

Finally, one study probed the cerebellum and found an abolishment of practice-dependent improvements in response time in a Sternberg task, regardless of whether the anode or the cathode was placed over the cerebellum (and the other electrode over the vertex) ( Ferrucci et al., 2008 ). The contribution of the cerebellum to STM was also probed with single-pulse TMS by Desmond and colleagues (2005) , who also found a negative effect on response time in the Sternberg task. Whether other cerebellar stimulation paradigms can induce an enhancement of STM remains unexplored.

G eneral memory and learning

Researchers attempting to enhance learning processes have targeted various neural regions. Such diverse approaches again render it difficult to single out a pattern regarding stimulatory condition, mechanisms, and outcome. Most studies have applied tDCS during the learning phase, and most have targeted the left DLPFC or other left prefrontal areas. Generally, studies report memory improvement following tDCS with the anode over DLPFC ( Kincses et al., 2004 ; Javadi and Walsh, 2012 ; Javadi et al., 2012 ) or other prefrontal areas ( De Vries et al., 2010 ), and worsening memory after tDCS with the cathode over DLPFC ( Elmer et al., 2009 ; Hammer et al., 2011 ; Javadi and Walsh, 2012 ; Javadi et al., 2012 ) or other prefrontal areas ( Vines et al., 2006 ). However, in interpreting their results, investigators have often made the overly simplistic assumption that the effects of tDCS can be accounted for by the neurobiological effect of one of the electrodes, the anode enhancing and the cathode suppressing activity in the brain area under them. Yet, it is important to remember that tDCS is not monopolar and that all electrodes are active. Thus the brain is exposed to a flow of current with opposite faradizing effects of the anode and the cathode. Therefore, to speak of anodal tDCS or cathodal tDCS is inaccurate.

Few studies have targeted right prefrontal areas. One study reported no effects in an episodic verbal memory task after tDCS with either anode or cathode over the right prefrontal region ( Elmer et al., 2009 ). Two studies showed that the learning process of threat detection in a virtual reality environment and the time required to learn this skill can be improved following tDCS with the anode over the right prefrontal ( Bullard et al., 2011 ; Clark et al., 2012 ) or right parietal region ( Clark et al., 2012 ). Furthermore, Bullard and colleagues (2011) found that applying tDCS at the beginning of the learning phase significantly enhanced learning in comparison with findings in experienced learners (after 1 hour of training).

Bilateral stimulation (anode and cathode over homologous areas of either hemisphere) has been applied in a few studies ( Marshall et al., 2004 , 2011 ; Boggio et al., 2009 ; Chi et al., 2010 ; Cohen Kadosh et al., 2010 ; Penolazzi et al., 2010 ; Jacobson et al., 2012 ). Jacobson and coworkers (2012) applied bilateral tDCS (anodal left, cathodal right, or vice versa) over the parietal lobe during encoding. They found improved verbal memory only when the anode was placed over the left hemisphere and the cathode over the right hemisphere. Another study investigating the contribution of the parietal cortex to numerical learning applied bilateral tDCS during a training phase of 6 days ( Cohen Kadosh et al., 2010 ). While right-anodal/left-cathodal stimulation improved learning significantly, right-cathodal/left-anodal stimulation decreased learning compared with sham tDCS.

Penolazzi and colleagues (2010) applied bilateral tDCS (anode left and cathode right, or vice versa) over the frontotemporal cortex during encoding and found facilitated recall of pleasant images after right-anodal/left-cathodal tDCS, whereas left-anodal/right-cathodal tDCS facilitated recall of unpleasant images. These results support a theoretical model (specific valence hypothesis) according to which the right and left hemispheres are specialized in the processing of unpleasant and pleasant stimuli respectively. Another group applying bilateral stimulation (anodal left, cathodal right, or vice versa) over the anterior temporal lobe assessed visual memory ( Chi et al., 2010 ) and also reported an improvement in memorizing different types of shape after right-anodal/left-cathodal stimulation, but no effects when applying an inverse stimulation pattern.

One set of studies has investigated effects of bilateral anodal stimulation over DLPFC during sleep and wakefulness. In their first study, Marshall and colleagues (2004) reported an improvement of memory consolidation when applying intermittent (on/off 15 seconds) anodal tDCS simultaneously over both DLPFCs during slow-wave (nonrapid eye movement, non-REM) sleep but not during wakefulness. In a second study they investigated state-dependent effects, and found enhanced theta activity when transcranial slow oscillation stimulation (tSOS) was applied during wakefulness ( Kirov et al., 2009 ). Memory enhancement occurred only when tSOS was applied during learning, but not after learning. In their third study, Marshall and colleagues (2011) applied anodal theta-tDCS (tDCS oscillating at 5 Hz) during REM sleep and non-REM sleep, which led to increased gamma-band activity and decreased memory consolidation respectively. The data from these studies illustrate the potential of transcranial current stimulation at specific stimulation frequencies selectively to modulate specific brain oscillations. This NBS method provides an interesting approach for investigating the relation between cortical brain rhythms, sleep-related processes, and memory functions.

Some studies have reported apparently contradictory results, highlighting the need for further investigation of the mechanisms of action underlying tDCS and TMS. Boggio et al. (2009) found decreased “false memories” utilizing anodal tDCS over the left anterior temporal lobe, or bilateral (left-anodal/right-cathodal) tDCS. However, the same researchers reported a nearly identical effect after applying 1-Hz rTMS over the same region, a protocol that is believed to suppress activity of the targeted brain area ( Gallate et al., 2009 ). Of course, it is possible that the behavioral effect might be related to trans-synaptic network effects, rather than being mediated by the targeted brain region. Indeed, a study using single-pulse TMS reported a facilitatory effect on verbal memory after stimulating the right inferior PFC ( Kahn et al., 2005 ), presumably due to interhemispheric paradoxical facilitation effects. This would be consistent with another study that found an improvement in verbal memory after stimulating the left inferior PFC with 7-Hz rTMS bursts ( Köhler et al., 2004 ). Furthermore, a paired-pulse protocol known to induce facilitatory effects led to memory improvements after stimulation of the left and right DLPFC in verbal as well as nonverbal episodic memory. The combination of stimulation techniques and other methods, such as EEG and fMRI, allows their inherent advantages to be combined to help answer these open questions.

Elderly healthy subjects

Basic memory research includes mostly young and healthy subjects. However, one of the key topics in the domain of NBS research concerns the changes of interhemispheric balance and the increased compensatory recruitment of brain areas with aging. As memory represents an overarching topic for the elderly, it is crucial to promote research that investigates these changes and provides information as to how to enhance memory functions. Furthermore, research with healthy elderly subjects is vital if we want to translate it into the clinical setting, as patients with memory deficits are mostly older. A newly emerging field has started to investigate memory enhancement in elderly subjects and underlying models ( Rossi et al., 2004 ; Solé-Padullés et al., 2006 ; Manenti et al., 2011 ; Flöel et al., 2012 ).

The “Hemispheric Asymmetry Reduction in Older Adults” (HAROLD) model states that prefrontal activity during cognitive performance becomes less lateralized with advancing age ( Cabeza, 2002 ). Manenti and colleagues investigated the differential assumptions of the HERA model (young subjects) and the HAROLD model (elderly subjects), suggesting that hemispheric asymmetry is reduced with age. Interestingly, they could show that low-performing elderly subjects continue showing prefrontal asymmetry, whereas high-performing elderly individuals show reduced asymmetry indicative of compensatory mechanisms ( Manenti et al., 2011 ).

Although lateralized activations within the PFC can be observed in younger subjects during episodic memory tasks ( Rossi et al., 2001 ), this asymmetry vanishes progressively with advancing age, as indicated by bilateral interference effects ( Rossi et al., 2004 ).

Conversely, the predominance of left DLPFC effect during encoding was not abolished in older subjects, indicating its causal role for encoding along the lifespan. However, this study did not differentiate between high- and low-performing subjects. Another study supported the assumption that higher performance is associated with more bilateral recruitment of brain areas and that stimulation may be able to promote the recruitment of additional brain areas to compensate for age-related decline. Solé-Padullés and colleagues (2006) found improved performance in associative learning after 5-Hz offline rTMS, which was accompanied by additional recruitment of right prefrontal and bilateral posterior brain regions.

A tDCS study showed improvements in spatial learning and memory in elderly subjects (mean 62 years) when stimulating during encoding ( Flöel et al., 2012 ). Anodal stimulation over the right temporoparietal cortex improved free recall 1 week later compared with sham stimulation. No immediate learning differences were observed, which indicates that retention (less decay) rather than encoding was affected by the stimulation.

To summarize, several studies have found different results following the stimulation of the DLPFC in young and elderly healthy subjects in accordance with the HAROLD model ( Cabeza, 2002 ). These differences could be due to changes in interhemispheric balance and recruitment of different brain areas for the same tasks, which could arise due to compensatory mechanisms. It remains to be further elucidated whether these changes reflect local or distributed mechanisms, whether compensatory recruitment of additional brain areas is associated with higher performance levels and could be enhanced by NBS.

Compared with the wealth of studies that have been done with healthy and mostly young subjects, studies on patients are rather sparse (see Table 55.1 ). The evidence is encouraging and calls for further investigation of the combined application of NBS and neuropsychological therapy. Besides behavioral measures, these studies should ideally include other measurements, such as assessment of brain plasticity or memory-specific neurophysiological outcomes. The work on patients with stroke is very preliminary, and more studies with larger patient numbers and better control of lesion location are needed. In one crossover, sham-controlled study, Jo et al. (2009) applied tDCS with the anode over the left DLPFC (and the cathode over the contralateral supraorbital area) in a 2-back task to 10 patients with unilateral, right-hemispheric, ischemic, or hemorrhagic strokes (1–4 months poststroke). After a single stimulation session, performance accuracy but not reaction time improved significantly. Enhancement of memory functions has been more extensively investigated in patients with AD and Parkinson’s disease (PD). These findings provide evidence that NBS could be a safe and useful tool in restoring/compensating brain functions through activation of primary and compensatory networks that underlie memory functions.

A lzheimer’s disease

A few studies have demonstrated effects of NBS on cognitive functions in AD (6 TMS, 3 tDCS). The first studies that used NBS in AD looked primarily at language and not memory functions. Cotelli and colleagues used rTMS (20 Hz) over the left and right DLPFC and reported positive effects for both hemispheres. They applied a single online session of rTMS in two crossover, sham-controlled studies ( Cotelli et al., 2006 , 2008 ). In the first study they reported improved accuracy in action naming, but not object naming, for all patients ( Cotelli et al., 2006 ). In the second study they could replicate the positive results for action naming; however, object naming also improved significantly, although only in moderately to severely impaired patients ( Cotelli et al., 2008 ). The authors hypothesized that the lack of improvement in object naming may be due to a ceiling effect. Furthermore, the bilateral effect could have been due to compensatory activation of right hemispheric resources.

In a third placebo-controlled study the same authors tested various functions, including memory, executive functions, and language in patients with moderate AD ( Cotelli et al., 2011 ). This study entailed 4 weeks of daily sessions of 20-Hz rTMS to the left DLPFC. Although they found significant improvements in sentence comprehension after 10 sessions (with no further improvement after 20 sessions), they did not find any improvements in memory and executive functions ( Cotelli et al., 2011 ). This lack of improvement could be due to the fact that the patients were not doing any specific concomitant cognitive training. Alternatively, the lack of memory effects could be related to the targeted brain region.

Bentwich and colleagues (2011) interleaved cognitive training and rTMS (10 Hz) during 30 sessions while stimulating six different brain regions (Broca, Wernicke, right and left DLPFC and parietal cortices). During each session three of these regions were stimulated while patients did cognitive tasks that were developed to fit each of these regions. Improvements in cognitive functions were significant, as measured using the cognitive subscale of the Alzheimer’s Disease Assessment Scale (ADAS-Cog), and were maintained for 4.5 months after the training. A case report ( Haffen et al., 2012 ) showed an improvement in episodic memory (free recall) and processing speed following 10 sessions of rTMS (10 Hz) over the left DLPFC. These are open trials and, obviously, sham-controlled interventions are needed. However, the results are promising and warrant follow-up. In a sham-controlled trial, Ahmed and colleagues (2012) assigned 45 patients with AD to three different treatment groups to study the effects of high- or low-frequency rTMS (20 Hz, 1 Hz), or sham stimulation. Patients received treatment on 5 consecutive days without combined cognitive training. Mildly to moderately impaired patients receiving high-frequency rTMS improved significantly on all scales (Mini Mental State Examination (MMSE), Instrumental Daily Living Activity Scale, Geriatric Depression Scale), and maintained these improvements for 3 months. However, severely impaired patients did not respond to the treatment.

Two crossover studies applied tDCS for one session and reported improvements in visual recognition memory following stimulation of the left DLPFC and temporoparietal cortex (TPC) ( Boggio et al., 2009 ), and in word recognition following stimulation of the bilateral TPC ( Ferrucci et al., 2008 ). In the first study, the authors applied 15 minutes of anodal, cathodal, and sham stimulation over bilateral TPC on three different sessions in patients with mild AD. While anodal tDCS led to an improvement, cathodal stimulation led to impairments in word recognition. No effects were observed in a visual attention task ( Ferrucci et al., 2008 ). In the second study, mildly to moderately impaired AD patients received anodal tDCS over the left DLPFC, the left TPC, or sham stimulation. Stimulation over both DLFPC and TPC resulted in a significant improvement in visual recognition. No effects were observed on selective attention or a visual delayed match-to-sample task.

Possibly, tDCS-induced changes in cholinergic activity contributed to these improvements. A recent study reported a significant change of SAI (ISI 2 ms) in the motor cortex of healthy subjects after anodal stimulation, while the resting motor threshold and amplitudes of motor evoked potentials did not change ( Scelzo et al., 2011 ). This could explain the positive impact of tDCS on memory functions in the above-mentioned studies. Future studies measuring behavioral along with neurophysiological effects and exploring correlations between them would be desirable.

P arkinson’s disease

Two studies have applied TMS or tDCS with the aim of improving cognitive functioning in PD. The first study compared the effects of active or sham rTMS and fluoxetine or placebo in patients with PD with concurrent depression ( Boggio et al., 2005 ). The authors applied 15-Hz rTMS over the left DLPFC for 10 daily sessions, and assessed cognitive functions at baseline, and 2 and 8 weeks after the treatment. Treatments were not combined with cognitive training or psychotherapy. After 2 weeks both interventions led to similar improvements in the Stroop Test and the Wisconsin Card Sorting Test (executive functions), and the Hooper (visuospatial functions). Furthermore, depression rates improved significantly in both groups. However, no improvements were reported in STM or WM (digits forward and backward). Eight weeks after treatment, these improvements declined slightly but remained significant.

The second study found improved accuracy in a 3-back task during a single session of anodal tDCS over the left DLPFC. Improvement was significant at a stimulation intensity of 2 mA but not at 1 mA ( Boggio et al., 2006 ).

Cognitive impairments in PD are often associated with depression symptoms, which occur in about 35% of patients. Furthermore, dementia is common in these patients with a point prevalence of 30% ( Aarsland and Kurz, 2010 ). Further studies are needed to investigate underlying processes leading to cognitive impairments. Moreover, studies should evaluate the efficacy of repetitive NBS in combination with cognitive training for this patient population.

A quickly growing number of studies is using NBS applications to study the underlying neurobiological substrates of memory functions, to investigate the use of TMS as a diagnostic tool, and the application of NBS to enhance memory functions. To date, most studies have used TMS to probe underlying memory processes and their causal and temporal relationships, whereas TMS, tDCS, and other forms of transcranial current stimulation are being used to enhance memory functions in healthy as well as patient populations. The combination of NBS with other methods, such as EEG and fMRI, enables the measurement of behavioral along with neurophysiological effects; the exploration of correlations between them is desirable to advance our neurobiological understanding and optimize future interventions.

ACKNOWLEDGMENTS

A.P.-L. serves on the scientific advisory boards for Nexstim, Neuronix, Starlab Neuroscience, Neosync, and Novavision, and is listed as an inventor on several issued and pending patents on the real-time integration of transcranial magnetic stimulation (TMS) with electroencephalography (EEG) and magnetic resonance imaging (MRI). Work on this study was supported by grants from the National Center for Research Resources: Harvard Clinical and Translational Science Center/Harvard Catalyst (UL1 RR025758), and investigator-initiated grants from Nexstim Inc. and Neuronix. A.-K.B. was supported by the Young Academics Support of the University of Zurich, Switzerland. K.R. was supported by the Dean’s Summer Research Award Grant, Harvard University.

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