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research on memory retention

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research on memory retention

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Human memory retention and recall processes. A review of EEG and fMRI studies

Human memory is an important concept in cognitive psychology and neuroscience. Our brain is actively engaged in functions of learning and memorization. Generally, human memory has been classified into 2 groups: short-term/working memory, and long-term memory. Using different memory paradigms and brain mapping techniques, psychologists and neuroscientists have identified 3 memory processes: encoding, retention, and recall. These processes have been studied using EEG and functional MRI (fMRI) in cognitive and neuroscience research. This study reviews previous research reported for human memory processes, particularly brain behavior in memory retention and recall processes with the use of EEG and fMRI. We discuss issues and challenges related to memory research with EEG and fMRI techniques.

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A powerful way to improve learning and memory

Jeffrey Karpicke, PhD

The movie "Inside Out" (2015) takes us inside the mind of its young protagonist, an 11-year-old girl named Riley, and depicts memory in a way that is sure to resonate with many people. In Riley's mind, her memories are objects — globes colored with emotions — that are stored in a mental space, just as physical objects are stored in a physical space. When Riley experiences an event and creates a new memory, a new globe is produced in her mind, rolling down a ramp like a ball returning in a bowling alley. When Riley re-experiences a past event, a globe is placed in a projector and events are replayed, projected on a screen in her mind. Cognitive psychologists refer to the mental processes involved in the creation of new memories and the recovery of past memories as encoding and retrieval, respectively.

The depiction of the mind in "Inside Out" follows centuries of thought on how mind and memory work. Throughout history, scholars have used a common metaphor to talk about memory: The mind is a vast storehouse or space; memories are objects stored in that space; and retrieving a memory is akin to searching for and finding an object in a physical space (Roediger, 1980). To learn something new, according to this view, the challenge lies in getting knowledge "in" one's mental space. Getting it back "out" when needed is important, too, but learning is usually identified with the encoding of new knowledge in memory. Retrieval is assumed to be neutral for learning; retrieval is needed to assess what a person has learned, but retrieval processes themselves are not thought to produce learning.

Recent advances in the science of learning and memory have challenged common assumptions about how learning happens. Specifically, recent work has shown that retrieval is critical for robust, durable, long-term learning. Every time a memory is retrieved, that memory becomes more accessible in the future. Retrieval also helps people create coherent and integrated mental representations of complex concepts, the kind of deep learning necessary to solve new problems and draw new inferences. Perhaps most surprisingly, practicing retrieval has been shown to produce more learning than engaging in other effective encoding techniques (Karpicke & Blunt, 2011). This approach, which recognizes the central role of retrieval processes in learning and aims to develop new learning strategies based on retrieval practice, is referred to as retrieval-based learning.

Retrieval Creates Learning

Research dating back a century has shown that retrieval contributes to learning (for a historical review, see Roediger & Karpicke, 2006a), but the past decade has seen a renewed, intense focus on exploring the benefits of retrieval for learning. This recent research has established that repeated retrieval enhances learning with a wide range of materials, in a variety of settings and contexts, and with learners ranging from preschool ages into later adulthood (Balota, Duchek, Sergent-Marshall & Roediger, 2006; Fritz, Morris, Nolan & Singleton, 2007).

A word-learning experiment illustrates some key points about retrieval-based learning. In the experiment (Karpicke & Bauernschmidt, 2011), students learned a list of foreign language words (e.g., Swahili vocabulary words like "mashua — boat") across cycles of study and recall trials. In study trials, the students saw a vocabulary word and its translation on the computer screen, and in recall trials, they saw a vocabulary word and had to recall and type its translation. The students studied a list of vocabulary words, then attempted to retrieve the whole list, studied it again, retrieved it again, and so on across alternating study and retrieval practice blocks.

There were several different conditions in the experiment. In one condition, students simply studied the words once, without trying to recall them at all. In a second condition, students continued studying and recalling the words until they had recalled all of them once. After a word was successfully retrieved once, it was "dropped" from further practice — the students did not see it again in the learning session.

Other conditions in the experiment examined the effects of repeated retrieval practice. Once a word was recalled, the computer program required the students to practice retrieving the items three more times. One repeated retrieval condition had the three recall trials happen immediately, three times in a row. This condition, referred to as massed retrieval practice, is akin to repeating a new piece of information over and over in your head right after you experience it. Finally, in the last condition highlighted here, the students also practiced retrieving the words three times, but the repeated retrievals were spaced throughout the learning session. For instance, once a student correctly recalled the translation for mashua, the program moved on to other vocabulary words, but prompts to practice retrieval of the translation for mashua would pop up later on in the program. In this way, the retrieval opportunities were spaced throughout the learning session.

Figure 1. Data from Karpicke & Bauernschmidt (2011).

Retrieval Practice is Underappreciated as a Learning Strategy

If retrieval practice is such a potent learning strategy, one would hope that many learners would practice retrieval to learn many different things in many situations. However, as noted earlier, retrieval is not typically considered an important part of the learning process, and unfortunately, many learners do not practice retrieval as often or as effectively as they could.

An emphasis on getting knowledge in memory shows up on surveys of students' learning strategies. In one survey (Karpicke, Butler & Roediger, 2009), college students were asked to list the strategies they use while studying and to rank-order the strategies. The results, shown in Figure 2, indicate that students' most frequent study strategy, by far, is repetitive reading of notes or textbooks. Active retrieval practice lagged far behind repetitive reading and other strategies (for a review of several learning strategies, see Dunlosky, Rawson, Marsh, Nathan & Willingham, 2013). A wealth of research has shown that passive repetitive reading produces little or no benefit for learning (Callender & McDaniel, 2009). Yet not only was repetitive reading the most frequently listed strategy, it was also the strategy most often listed as students' number one choice, by a large margin.

Figure 2. Student study strategy usage. Survey data from Karpicke, Butler, & Roediger (2009).

Figure 2. Student study strategy usage. Survey data from Karpicke, Butler, & Roediger (2009).

Why don't learners use repeated retrieval practice more frequently? Many students view retrieval as a "knowledge check"; they test themselves to see if they know something, rather than out of the belief that practicing retrieval itself will help them learn. This means that many students will use a "one-and-done" strategy: If they can recall something once, they believe they have learned it, so they remove it from further practice. Many students study this way when they regulate their own learning (Karpicke, 2009), even though their long-term learning will not benefit from repeated retrieval practice. Instead, a one-and-done strategy will produce long-term performance similar to the recall-once condition in Figure 1. There is some evidence that instructing learners about the benefits of retrieval leads students to report using retrieval practice more frequently when they study on their own (Einstein, Mullet & Harrison, 2012), but the best ways to influence students to practice retrieval remain to be discovered.

Practicing Retrieval Promotes Meaningful Learning

Perhaps another reason retrieval practice is not used more widely is because repeated retrieval may seem like "rote learning." Rote learning — simple memorization based on repetition — is short-lived, poorly organized and does not support the ability to transfer knowledge, make inferences or solve new problems. The outcome of rote learning is obviously not what students and educators aim for. Meaningful learning is essentially the opposite of rote learning: It is long-lasting and durable, coherent and well organized, and supports transfer, inferencing and problem solving. In fact, the past decade of research on retrieval-based learning has firmly established that retrieval practice promotes meaningful learning.

Retrieval-based learning may be a more effective means of achieving meaningful learning than other popular active learning strategies. In one example of this (Karpicke & Blunt, 2011), students studied educational texts about science topics using one of two strategies. In a retrieval practice condition, students read a text, then set it aside and spent time recalling and writing down as much as they could remember from it (Roediger & Karpicke, 2006b). They then reread the text and recalled it a second time. In a second condition, students created concept maps while they read the texts. Concept maps are node-and-link diagrams that require learners to think about the relational and organizational structure of materials (Novak, 2013). The students spent the same amount of time studying in the two conditions; the difference was whether they created concept maps or practiced actively retrieving while learning.

Figure 3 shows the results of two different final assessments given one week after the learning session. On one assessment, students answered two types of short-answer questions aimed at measuring meaningful learning: verbatim questions, which assessed concepts stated directly in the texts, and inference questions, which required students to make new connections across concepts. On another assessment, the final assessment involved creating a concept map, because concept mapping is often used as an assessment of the coherence and integration of students' knowledge. On the final verbatim and inference questions and on the final concept map assessment, practicing retrieval during learning produced the best performance, even better than studying the material by making concept maps.

Figure 3. Data adapted from Karpicke & Blunt (2011) (from Figure 2, panels A and C).

Figure 3. Data adapted from Karpicke & Blunt (2011) (from Figure 2, panels A and C).

Several additional studies have established that retrieval practice promotes meaningful learning. Retrieval practice enhances the learning of educationally relevant materials, including educational texts, multimedia presentations, material explained in classroom lectures and a variety of other complex concepts (Jensen, McDaniel, Woodard & Kummer, 2014; Johnson & Mayer, 2009; Larsen, Butler, Lawson & Roediger, 2013; Lyle & Crawford, 2011; Roediger, Agarwal, McDaniel & McDermott, 2011). Retrieval practice also supports students in making inferences, solving new problems and transferring knowledge (Butler, 2010; Chan, 2009; Hinze & Wiley, 2011; McDaniel, Howard & Einstein, 2009; Smith & Karpicke, 2014). Retrieval-based learning is an effective method for improving meaningful learning.

Creating Retrieval-based Learning Activities

Perhaps the best aspect of retrieval-based learning is that it is free. Although there are sophisticated tools that can be used to implement retrieval practice, like classroom clicker systems (Roediger et al., 2011) and other computer-based learning systems (Grimaldi & Karpicke, 2014; Lindsey, Shroyer, Pashler, & Mozer, 2014), retrieval practice does not require special equipment or technology. The essence of retrieval-based learning is taking material you are trying to learn, setting it aside, and spending time actively retrieving the information.

Existing educational activities can be converted into retrieval-based learning activities. For instance, answering questions and taking quizzes are effective ways to practice retrieval. In some circumstances, students might answer questions on quizzes or practice worksheets by looking up answers in their notes or books, rather than by attempting to retrieve the answers. One study directly compared this type of open-book questioning to closed-book conditions in which students were required to retrieve the answers to questions rather than looking them up (Agarwal, Karpicke, Kang, Roediger & McDermott, 2008). Answering questions in open-book conditions led to more forgetting over one week than did attempting to retrieve the answers, closed-book and then studying the answers. In other words, closed-book quizzes, which required retrieval practice, were more effective than open-book quizzes, which did not require learners to engage in retrieval.

Another study (Blunt & Karpicke, 2014) examined the effectiveness of using concept mapping as a retrieval practice activity. In the experiment, students read texts about science topics and then created concept maps either with or without viewing the texts. In other words, some students created maps while studying the texts whereas other students had to engage in retrieval to create their maps. On a short-answer assessment one week after the learning session, students did better when they had learned by creating concept maps without viewing the texts, as a retrieval practice activity, than by creating maps while studying the texts. Thus, educational activities can be enhanced when they involve retrieval-based learning.

Take Home Points About Retrieval-based Learning

This article has made the case for four take-home points about retrieval-based learning:

Agarwal, P.K., Karpicke, J. D., Kang, S.H.K., Roediger, H.L., & McDermott, K.B. (2008). Examining the testing effect with open- and closed-book tests. Applied Cognitive Psychology, 22 (7), 861-876. doi:10.1002/acp.1391.

Balota, D.A., Duchek, J.M., Sergent-Marshall, S.D., & Roediger, H.L. (2006). Does expanded retrieval produce benefits over equal-interval spacing? Explorations of spacing effects in healthy aging and early stage Alzheimer's disease. Psychology and Aging, 21 (1), 19-31. doi:10.1037/0882-7974.21.1.19.

Blunt, J.R., & Karpicke, J.D. (2014). Learning with retrieval-based concept mapping. Journal of Educational Psychology, 106 (3), 849-858. doi:10.1037/a0035934.

Butler, A.C. (2010). Repeated testing produces superior transfer of learning relative to repeated studying. Journal of Experimental Psychology: Learning, Memory, and Cognition, 36 (5), 1118-1133. doi:10.1037/a0019902, 10.1037/a0019902.supp.

Callender, A.A., & McDaniel, M.A. (2009). The limited benefits of rereading educational texts. Contemporary Educational Psychology, 34 (1), 30-41. doi:10.1016/j.cedpsych.2008.07.001.

Chan, J.C.K. (2009). When does retrieval induce forgetting and when does it induce facilitation? Implications for retrieval inhibition, testing effect, and text processing. Journal of Memory and Language, 61 (2), 153-170. doi:10.1016/j.jml.2009.04.004.

Dunlosky, J., Rawson, K.A., Marsh, E.J., Nathan, M.J., & Willingham, D.T. (2013). Improving students' learning with effective learning techniques: Promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, 14 (1), 4-58. doi:10.1177/1529100612453266.

Einstein, G.O., Mullet, H.G., & Harrison, T.L. (2012). The testing effect: Illustrating a fundamental concept and changing study strategies. Teaching of Psychology, 39 (3), 190-193. doi:10.1177/0098628312450432.

Fritz, C.O., Morris, P.E., Nolan, D., & Singleton, J. (2007). Expanding retrieval practice: An effective aid to preschool children's learning. The Quarterly Journal of Experimental Psychology, 60 (7), 991-1004. doi:10.1080/17470210600823595

Grimaldi, P.J., & Karpicke, J.D. (2014). Guided retrieval practice of educational materials using automated scoring. Journal of Educational Psychology, 106 (1), 58-68. doi:10.1037/a0033208.

Hinze, S.R., & Wiley, J. (2011). Testing the limits of testing effects using completion tests. Memory, 19 (3), 290-304. doi:10.1080/09658211.2011.560121.

Jensen, J. L., McDaniel, M.A., Woodard, S.M., & Kummer, T.A. (2014). Teaching to the test ... or testing to teach: Exams requiring higher order thinking skills encourage greater conceptual understanding. Educational Psychology Review, 26 (2), 307-329. doi:10.1007/s10648-013-9248-9.

Johnson, C.I., & Mayer, R.E. (2009). A testing effect with multimedia learning. Journal of Educational Psychology, 101 (3), 621-629. doi:10.1037/a0015183.

Karpicke, J.D. (2009). Metacognitive control and strategy selection: Deciding to practice retrieval during learning. Journal of Experimental Psychology: General, 138 (4), 469-486. doi:10.1037/a0017341.

Karpicke, J.D., & Bauernschmidt, A. (2011). Spaced retrieval: Absolute spacing enhances learning regardless of relative spacing. Journal of Experimental Psychology: Learning, Memory, and Cognition, 37 (5), 1250-1257. doi:10.1037/a0023436.

Karpicke, J.D., & Blunt, J.R. (2011). Retrieval practice produces more learning than elaborative studying with concept mapping. Science, 331 (6018), 772-775. doi:10.1126/science.1199327.

Karpicke, J.D., Butler, A.C., & Roediger, H.L. (2009). Metacognitive strategies in student learning: Do students practise retrieval when they study on their own? Memory, 17 (4), 471-479. doi:10.1080/09658210802647009.

Karpicke, J.D., & Roediger, H.L. (2008). The critical importance of retrieval for learning. Science, 319(5865), 966-968. doi:10.1126/science.1152408.

Larsen, D.P., Butler, A.C., Lawson, A.L., & Roediger, H.L., III. (2013). The importance of seeing the patient: Test-enhanced learning with standardized patients and written tests improves clinical application of knowledge. Advances in Health Sciences Education, 18 (3), 409-425. doi:10.1007/s10459-012-9379-7.

Lindsey, R.V., Shroyer, J.D., Pashler, H., & Mozer, M.C. (2014). Improving students' long-term knowledge retention through personalized review. Psychological Science, 25 (3), 639-647. doi:10.1177/0956797613504302.

Lyle, K.B., & Crawford, N.A. (2011). Retrieving essential material at the end of lectures improves performance on statistics exams. Teaching of Psychology, 38(2), 94-97. doi:10.1177/0098628311401587.

McDaniel, M.A., Howard, D.C., & Einstein, G.O. (2009). The read-recite-review study strategy: Effective and portable. Psychological Science, 20 (4), 516-522. doi:10.1111/j.1467-9280.2009.02325.x.

Novak, J.D. (2013). Concept mapping. In J. Hattie & E.M. Anderman (Eds.), International Guide to Student Achievement (pp. 362-365). New York, NY: Routledge/Taylor & Francis Group.

Pyc, M.A., & Rawson, K.A. (2010). Why testing improves memory: Mediator effectiveness hypothesis. Science, 330 (6002), 335. doi:10.1126/science.1191465.

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Roediger, H.L., Agarwal, P.K., McDaniel, M.A., & McDermott, K.B. (2011). Test-enhanced learning in the classroom: Long-term improvements from quizzing. Journal of Experimental Psychology: Applied, 17 (4), 382-395. doi:10.1037/a0026252.

Roediger, H.L., & Karpicke, J.D. (2006a). The power of testing memory: Basic research and implications for educational practice. Perspectives on Psychological Science, 1 (3), 181-210. doi:10.1111/j.1745-6916.2006.00012.x.

Roediger, H.L., & Karpicke, J.D. (2006b). Test-enhanced learning: Taking memory tests improves long-term retention. Psychological Science, 17 (3), 249-255. doi:10.1111/j.1467-9280.2006.01693.x.

Smith, M.A., & Karpicke, J.D. (2014). Retrieval practice with short-answer, multiple-choice, and hybrid tests. Memory, 22 (7), 784-802. doi:10.1080/09658211.2013.831454.

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Memory Retention

Memory retention can then be assessed at any time by returning the animal to the conditioning chamber and measuring its latency to enter the dark (shock) compartment.

From: Memory Reconsolidation , 2013

Related terms:

Learning Theory and Behaviour

D. Eisenhardt , N. Stollhoff , in Learning and Memory: A Comprehensive Reference , 2008

1.27.2.4.2 Reconsolidation of the CSM

CSM retention can be observed 24 h after spaced training when the crabs are exposed to the VDS in the training context. When the crabs are exposed to the training context without the presentation of the VDS, a conditioned response is not elicited ( Pedreira et al., 2002 ). Nevertheless, this exposure to the training context has an impact on subsequent memory retention . Namely, the combination of a 5-min context exposure 24 h after training with an injection of protein synthesis inhibitor leads to the inhibition of CSM memory retention 1 day later ( Pedreira et al., 2002 ) ( Figure 14(a) ). The reconsolidation phenomenon has been induced accordingly.

research on memory retention

Figure 14 . The crab Chasmagnathus granulatus : Reconsolidation and consolidation of extinction memory depend on the duration of the reexposure to the training context. Day 1: Training with 15 9-s presentations of the visual danger stimulus (striped bar), separated by 3 min; Day 2: Systemic injection (arrow) of Cycloheximid (CYX; violet arrow and bars) or saline (green arrow and bars) 1 h prior to reexposure to the training context trapeze for either 5 min (reconsolidation) or 60 min (consolidation of extinction); Day 3: Memory retention test: a single 9-s VDS presentation (striped bar). A significant difference between the untrained group (U) and the trained group (T) (U > T) at the memory retention test indicates memory retention. The trapeze stands for the container where a crab is placed during each of the experimental phases. Adapted from Figure 2 in Pedreira ME and Maldonado H (2003) Protein synthesis subserves reconsolidation or extinction depending on reminder duration. Neuron 38: 863–869.

Invertebrate Learning and Memory

Margaret Hastings , ... Wayne S. Sossin , in Handbook of Behavioral Neuroscience , 2013

PKC Apl II Responses Can Differentiate between Spaced and Massed Applications of 5-HT in Sensory Neurons

Memory retention is highly sensitive to the pattern of trials used during training. Training distributed over time (spaced training) is superior to training presented with little or no rest intervals (massed training) at generating long-term memories. 93–97 In culture, spaced applications of 5-HT are superior to massed applications at generating long-term facilitation of cultured sensorimotor neuron synapses. 98 PKC Apl II activation is highly sensitive to the pattern of 5-HT application. 99 Spaced applications lead to desensitization of PKC Apl II translocation by a PKA-dependent mechanism, whereas massed application leads to persistent translocation of PKC Apl II. 99 Furthermore, regulation of PKC translocation is mediated by competing feedback mechanisms that act through protein synthesis. 99 Mathematical modeling of the PKC Apl II response to the different patterns of stimulation allowed the following to be determined: (1) increased desensitization due to PKA-mediated heterologous desensitization was coupled to a faster recovery than the homologous desensitization that occurs in the absence of PKA activity, and (2) the major determinant of how the system responds to spacing is the production and degradation rate of proteins. 100 Indeed, our results suggested that increased desensitization during spaced applications of 5-HT was due to the short half-life of a hypothetical protein, which prevented homologous desensitization. One pulse of 5-HT would synthesize this protein, which would protect against desensitization for the short period before it was degraded. 100 Massed application of 5-HT would constantly replenish this protein, whereas spaced applications of 5-HT, with an interapplication interval longer than the half-life of the protein, would overshoot the protective period and cause increased desensitization. 100

2D and 3D Educational Contents

Aamir Saeed Malik , Hafeez Ullah Amin , in Designing EEG Experiments for Studying the Brain , 2017

7.5.6.2 Learning Task (3D Animation)

For learning and memory retention purposes, this task consisted of 3D stereoscopic animations of the biology course. To avoid participants with background knowledge regarding the animations, participants were recruited having no or very little knowledge of biology. Thus, the participants would find the biology animations to be new knowledge and during watching the content, new memory traces would be formed. In this task, three animations were shown and each animation was repeated three times. The duration of the animated contents was approximately 10 minutes and it was repeated three times; hence, approximately 30 minutes were spent in the learning task. This task was used in the first three sessions. However, in the second and third sessions, the learning contents were presented once, i.e., for 10 minutes only.

Memory and Emotion

Mary Howes , Geoffrey O'Shea , in Human Memory , 2014

Emotion as a factor in memory retention is reviewed, beginning with the Yerkes-Dodson Law and progressing to coverage of the effects of emotion on episodic recall. Critical issues concerning the conceptualization of emotion are examined such as the distinction between the effects of emotion vs. extra rehearsal of emotion-inducing events, as well as ecological differences between the experience of real life emotional events and those simulated under laboratory conditions. The mechanisms by which emotion may operate to strengthen memory coding are considered in the case of flashbulb memories, the reoccurring memories of those experiencing post-traumatic stress disorder, and the role of the amygdala in influencing consolidation and ongoing storage of emotional content. Schank’s (1982, 1982) model detailing the relationship between goals and memory content is further considered with respect to the relation between emotion and the generation of goals. The chapter concludes by posing a number of questions about the emotional nature of memory and the implications of these questions for a definitive understanding of human memory.

Ken Lukowiak , Sarah Dalesman , in Handbook of Behavioral Neuroscience , 2013

Population Differences

As mentioned previously, our discovery of population differences in memory retention was purely by chance, but it has provided an intriguing new avenue for research. Whereas several of the populations we sampled exhibit similar LTM retention as that of the Dutch laboratory strain, a few have enhanced LTM formation. 28,42,43,119 For example, following a 0.5-hr operant training session, the Dutch strain and other populations exhibit ITM lasting only approximately 3 hr; 40,119 however, following the same training procedure, our ‘smart’ snails exhibit LTM lasting approximately 5 days. 27 Whereas heritable differences in learning and memory are found among individuals within a population in other invertebrate species, 120,121 Lymnaea is the first invertebrate species in which distinct differences in LTM-forming abilities have been found among natural populations. Although we doubt very much that it will be the only invertebrate in which such population differences are found, we intend to take advantage of what is so far a unique opportunity to develop an understanding of the mechanisms that control these differences and how they affect a population’s ability to respond to memory-altering stressors. Thus far, we have focused on proximate causes of differentiation among populations. In addition to differences in memory retention, do these animals differ in other behavioral or physiological traits?

Where we see enhanced LTM to reduce aerial respiration, one possible explanation is that these ‘smart’ individuals simply breathe more often and therefore receive a greater number of physical stimuli, resulting in stronger memory formation. Our results show that both the total breathing time in hypoxia or eumoxia in the absence of physical stimuli and the average number of stimuli they receive during a typical training session (i.e., the number of pokes in 30 min) do not differ significantly between animals exhibiting good or poor LTM-forming abilities. 28,43,119 Therefore, we cannot explain differences in memory retention simply by the differences in breathing rate among populations.

An alternative way to assess differences between populations is by assessing the underlying neurophysiology. Following operant training to reduce aerial respiration, there are significant changes in some electrophysiological properties of RPeD1, a neuron that forms part of the CPG that controls aerial respiration. In semi-intact preparations, electrophysiological changes correlate well with changes in breathing behavior 122,123 and also differ depending on whether ITM or LTM is formed. 26 We compared electrophysiological properties in RPeD1 in two Canadian populations, one exhibiting enhanced LTM formation (TC1) and one exhibiting poorer LTM-forming capabilities (TC2) similar to the Dutch lab-bred strain. Our findings indicated that the ‘smart’ TC1 population showed very similar electrophysiological properties in RPeD1 in naive animals as those in the populations without enhanced memory, including TC2 and the Dutch laboratory strain, following 0.5-hr training. 26,28 Therefore, it seems that these ‘smart’ individuals form LTM more readily because they are already primed to do so. Currently, we do not know why or how RPeD1 is ‘primed’ in the ‘smart’ snails.

Having identified populations in both the United Kingdom and Canada that differ in memory-forming ability, we were interested in how these ‘smart’ snails would respond to environmental stressors relative to the Dutch population. Although we have previously demonstrated that ‘smart’ snails form LTM in the presence of predator kairomones, we have not previously published data demonstrating that they can extend memory retention beyond that seen in control conditions in the same way that we see in the Dutch strain and other wild populations. 43,119 It is possible that these ‘smart’ Lymnaea demonstrate enhance LTM because they are already at their maximum ability to form LTM following operant conditioning due to their primed state, whereas the Dutch snails are more plastic, only exhibiting maximum LTM formation in the presence of a stressor such as CE. Here, we present previously unpublished data from work with snails from a ‘smart’ population in the United Kingdom (Chilton Moor). These data demonstrate that whereas ‘smart’ snails form LTM lasting 5 but not 8 days in control conditions, 27 when we train them in predator cues (CE), we can extend this memory to at least 8 days ( Figure 21.4 ) . Therefore, the ‘smart’ snails are still able to show plasticity in LTM formation, despite starting in a primed state. 28

research on memory retention

Figure 21.4 . Smart snails and CE. Plotted are the mean number of attempted pneumostome openings and the standard error of the mean (SEM). (Left) In smart snails obtained from a pond in the Somerset Levels in the United Kingdom, a single 0.5-hr training session results in a memory that persists for up to 5 days. In lab-bred snails, a single 0.5-hr training session results in a memory that only persists for up to 3 hr. However, as shown on the right, predator detection even in these smart snails results in enhanced LTM formation because now memory persists for more than 8 days. In both panels, we present data showing that the yoked control procedure does not result in LTM when tested at the 24-hr mark. ** p <0.01..

In addition to assessing whether all populations respond to predator stress with enhanced LTM formation, which we now believe to be the case, we also wanted to assess whether they all respond to stressors that block memory in the Dutch strain. We therefore chose to assess the effects of crowding and low environmental calcium on LTM formation in two of our ‘smart’ populations, one from Canada (Trans-Canada 1 (TC1)) and one from the United Kingdom (Chilton Moor (CM)). We found that crowding the snails immediately prior to training blocked LTM in both our ‘smart’ populations, demonstrating the same phenotypic response as seen in the Dutch strain. 27 However, a low-calcium environment did not block LTM, as seen in the Dutch strain. 67,69,72 but did significantly shorten memory retention from 5 days to less than 72 hr. Therefore, although the directional effect on memory retention (i.e., reducing duration) was similar in the ‘smart’ snails to what is seen in the Dutch laboratory strain, the snails with enhanced memory appear to be more resistant to low calcium stress. Connections between stress resistance and cognitive abilities have been found elsewhere 120,124,125 and may prove to be highly conserved across species.

Based on our current work, it seems that all snail populations tested, whether they have enhanced memory-forming abilities or not, respond to ecologically relevant stressors in a similar directional pattern. We have been able to identify that LTM formation under control conditions, and also in stressed conditions, appears to vary consistently with memory phenotype (i.e., whether the population is classified as ‘smart’ or ‘dumb’). This is the case even when we compared populations situated on separate continents, in North America and Europe. To us, this indicates that whatever mechanism(s) is controlling LTM capabilities (e.g., causing changes in the properties of RPeD1), and also how memory is altered by stress, is highly conserved across the L. stagnalis species, showing similar patterns on both broad and narrow geographic scales.

Changing Brains

Seth A. Hays , ... Michael P. Kilgard , in Progress in Brain Research , 2013

4.3 Cognitive dysfunction

Aberrant plasticity is believed to underlie the hypersensitivity and abnormal memory retention that accompanies posttraumatic stress disorder (PTSD) ( Bremner et al., 2007; Peña et al., 2012 ), and reversal of this maladaptive plasticity may erase fear memory ( Sandkühler and Lee, 2013 ). As such, the ability to apply VNS to normalize the hypersensitive responses to stimuli may improve the symptoms of PTSD. A proof of principle study conducted by Peña and colleagues in a rat model of PTSD lends credence to this hypothesis. Rats were trained on an auditory fear conditioning task followed by extinction training with or without VNS ( Peña et al., 2012 ). Testing was conducted 1 day later to assess conditioned fear retention. VNS paired with extinction training resulted in a significant reduction of conditioned fear retention compared to extinction training without VNS. Unpaired VNS delivered shortly after training failed to reduce conditioned fear retention, suggesting that VNS must be temporally aligned with the behavioral experience. The beneficial effects of VNS are long-lasting, as conditioned fear remains reduced 2 weeks after the cessation of treatment. Additionally, VNS paired with extinction training was similarly effective at reducing a remote fear memory compared to extinction training without VNS. Although chronic VNS is known to confer anxiolytic effects ( Furmaga et al., 2011; George et al., 2008 ), this effect is not dependent on temporal specificity. Therefore, if VNS is exerting anxiolytic effects to reduce conditioned fear response, unpaired VNS delivery should be effective. However, because unpaired VNS fails to reduce the conditioned fear response, VNS is most likely acting through modulation of plasticity and memory rather than providing a generalized, nonspecific reduction in anxiety. Although much development remains, this study provides initial support that VNS paired with behavioral experience can improve extinction training.

Nanomedicine and Neuroprotection in Brain Diseases

Feng Niu , ... Hari Shanker Sharma , in Progress in Brain Research , 2021

9.7 Oxiracetam influence biobehavioral changes in CHI

The biobehavioral functions are evaluated in CHI group of animals using memory retention , sensory, motor and cognitive functions as well as locomotor functions and their modification with conventional or nanowired delivery of oxiracetam.

Memory function

Morris water maze (MWM) trained rats were allowed to search hidden platform under a pool of water from normal, CHI and conventional or nanowired oxiracetam treated group. Control group of rats either naïve or oxiracetam treated group find hidden platform within 4–5 s whereas, CHI rats took more time to find platform in the MWM as compared to controls. Thus, CHI progressively delays in finding the hidden platform depending on the deration of survival after trauma. Accordingly, 12, 18 and 22 s are needed to find CHI rats the hidden platform in MWM following 48, 96 and 192 h period, respectively ( P < 0.05 from control). This suggests that CHI progressively impairs memory function ( Fig. 3 ).

Treatment with conventional oxiracetam either 50 or 100 mg doses for 5 days results in significant attenuation of time in finding the platform in MWM depending on the dose. Thus, after 192 h of CHI rats treated with 50 mg oxiracetam find the platform in 15 s ( P < 0.05 from CHI 192 h) whereas under identical condition 100 mg oxiracetam treated injured rats could search the platform within13 s ( P < 0.05 from CHI 192 h). On the other hand TiO2-naowired oxiracetam 50 mg treated 192 h CHI rats find the platform in MWM within 8 s ( P < 0.05 from CHI 192 h) ( Fig. 3 ). This suggests that oxiracetam treatment in CHI improve memory function and this effect is the most pronounced with nanodelivery of the drug.

Cognitive function on Rota-Rod treadmill

Rota-Rod test evaluates the cognitive and motor functions of animals, as they have to balance on the moving platform from falling. This requires balance skill and motor function to stay on moving platform ( Sharma, 2006 ; Shiotsuki et al., 2010 ). Control rats when placed on the Rota-Rod treadmill for 120 s at 16 rpm thy could easily stay for 118 s. Treatment with conventional oxiracetam 50 and 100 mg or TiO2-nanowired oxiracetam 50 mg dose in normal rats did not affect their staying time over the Rota-Rod treadmill. These treated rats easily stayed over the treadmill for 116–118 s ( Fig. 11 ).

Fig. 11 . Effect of Na-TiO2 nanowired oxiracetam (NWOXR) (C, D) on behavioral functions on Rota-Rod treadmill (A), inclined plane angle (B), placement error (C), steps taken (D) on a mesh grid, stride length (E) and hind feet distances (F) while walking and gait analysis following concussive head injury (CHI) in the rat on 8th day after trauma. CHI was inflicted using a 0.224 N impact injury over Right parietal skull and the animals were allowed to survive for 1 week after the primary insult. Oxiracetam (OXR) was given in a dose of 50 or 100 mg/kg, i.p. once daily after 1 day of CHI for 5 days and the parameters were evaluated on the 8th day. For Na-TiO2 nanowired delivery of oxiracetam (NWOXR) 50 mg/kg (i.p.) was given under identical conditions. For details see text. Values are Mean ± SD of 6–8 rats at each point. MWM , Morris Water Maze platform search; OXR , oxiracetam; NWOXR , nanowired oxiracetam; TiO2-Na , sodium titanate nanowires. Volume swelling (%ƒ) was calculated according to Elliott and Jasper (1949) . * P < 0.05 from Control; § P < 0.05 from untreated CHI; # P < 0.05 from CHI + NWOXR (TiO2·Na) at respective group. ANOVA followed by Dunnett's test for multiple group comparison using one control. For details see text.

Placement of CHI rats on the Rota-Rod treadmill significantly reduced their staying time depending on the injury duration. Thus, 48 h CHI rats could not stay beyond 80 s on Rota-Rod treadmill whereas 96 and 192 h CHI animals maintained their stay for 72 and 60 s ( P < 0.05 from control), respectively ( Fig. 11 ). This suggests that CHI over time reduce the ability of cognitive and motor skill in animals.

However, treatment with oxiracetam 50 or 100 mg doses significantly enhanced staying time over the Rota-Rod treadmill after 192 h CHI in a dose dependent manner. Interestingly nanodelivery of oxiracetam 50 mg dose further enhanced the timing spent on the Rota-Rod treadmill in192 h CHI rats ( Fig. 11 ). Thus, oxiracetam 50 or 100 mg doses resulted in 192 h CHI rats to stay on the platform for 70 and 84 s, respectively ( P < 0.005 from control). Whereas TiO2-nanowired oxiracetam 50 mg resulted in 192 h CHI rats to stay over the Rota-Rod treadmill for 92 s ( Fig. 11 ).

Inclined plane angle test

Another cognitive test was used to evaluate in animals on an incline plane angle platform (60°) that allows normal rats to stay for 5 s ( Sharma, 2006 ). Traumatized animals could not stay on this steep angle platform and need to adjust the inclination for them to stay for 5 s. Control animals could stay at 60° for 5 s and also oxiracetam treated control group were fine at 58–60° on this test.

Subjection of CHI significantly reduced the angle of the platform that was progressive in nature. Thus, 48 h CHI animals require 48° to stay where as 96 and 120 h CHI need the angle to be lowered down to 40° and 30°, respectively ( P < 0.05 from control) ( Fig. 11 ).

Treatment with oxiracetam resulted in significant increase in plane angle following 192 h CHI that was dose dependent. Thus, 50 mg oxiracetam resulted in elevated platform of 38° and with 100 mg dose animals could stay on inclined plane angle of 44° ( P < 0.05 from CHI). However, TiO2-nanowired delivery of 50 mg oxiracetam treated animals could easily stayed at the incline plane of 52° after 192 h CHI ( P < 0.05 from CHI) ( Fig. 11 ).

Motor function and placement error

The motor skill was evaluated in trained animals by allowing them to walk on an inclined (45°) mesh platform where placement of forepaw was also investigated for placement error ( Sharma, 2006 ). These tests also suggest motor and cognitive skills of animals in walking on an inclined mesh platform with minimized placement error ( Sharma, 2006 ). In these situations number of steps taken and occurrence of placement error was evaluated.

Control group with or without oxiracetam treatment showed average 41–45 steps taken on the inclined mesh within 1 min. In these groups placement error was confined to 2–3 times in 60 s walking ( Fig. 11 ).

On the other hand animals with CHI the number of steps taken in a minute were significantly reduced and the occurrence of placement error increased significantly with time. Thus, 48 h CHI showed 32 steps/min that was reduced to 24 steps/min after 96 h of CHI. At 192 h of CHI only 18 steps/min were counted ( P < 0.05 from control). Whereas, the number of placement error occurred 6/min after 48 h CHI that increased further to 8 steps/min after 96 h and reduced to 4 steps/min after 192 h ( Fig. 11 ). It may be that less number of steps taken at this time is accountable of decreased placement error.

Gait and locomotion on stride length and hind feet distance

Brain damage caused by trauma or hyperthermia alters gait and locomotor behavior ( Sharma, 2006 ). We analyzed stride length and distances between hind feet to evaluate gait and locomotion disturbances in control, CHI with and without oxiracetam treatment.

In control group with or without treatment the stride length was very similar ranging from 125–128 mm and the distances between hind feet in two consecutive movements was within 45–48 mm. Rats subjected to CHI showed a progressive decrease in stride length and increase in distances between hind feet. Thus, 48 h after CHI the stride length was 90 mm and the distances between hind feet were 62 mm ( P < 0.05 from control). These values are further reduced at 96 h CHI (stride length 84 mm, distance between hind feet 78 mm) followed by 192 h CHI (stride length 78 mm, distance between hind feet 92 mm, P < 0.05 from control) ( Fig. 11 ).

Treatment with oxiracetam with 50 or 100 mg doses in CHI showed significant increase in stride length by 89 and 94 mm, respectively at 192 h ( P < 0.05 from CHI). The distances between hid feet were also reduced 65 and 60 mm after oxiracetam treatment with 50 and 100 mg doses, respectively after 192 h of CHI ( P < 0.05 from CHI). However, treatment with TiO2-nanowired oxiracetam 50 mg resulted in superior protective effect on the gait and locomotion at 192 h of CHI. Thus, in this group the stride length increased to 110 mm and the distance between hind feet was reduced to 54 mm from untreated CHI at this point ( P < 0.05 from CHI) ( Fig. 11 ).

The Context of Cognition: Emerging Perspectives

Xiaonan L. Liu , ... Charan Ranganath , in Psychology of Learning and Motivation , 2021

3.4 How sleep moderates the effects of retrieval practice on untested information

Prior studies suggested that sleep may produce a similar effect on memory retention with retrieval practice, so the relative benefits of testing over restudy might be neutralized when the practice phase is followed by a break with sleep ( Abel et al., 2019 ; Antony & Paller, 2018 ; Bäuml, Holterman, & Abel, 2014 ). Our recent study further showed that sleep might moderate the effects of retrieval practice on untested information ( Liu & Ranganath, 2021 ). In this study, subjects studied scene-word associations, and each scene was associated with two different words (target and non-target). We found that when target and semantically related non-target were studied in different blocks, i.e., temporal contexts, retrieval practice of targets impaired retention of non-targets if subjects were unable to sleep during the retention interval. However, it facilitated retention of these items if subjects were able to sleep.

Why does this process of connecting disparate memory episodes depend on sleep? We hypothesize that the selective deactivation of the prefrontal cortex (PFC) during sleep ( Hobson & Pace-Schott, 2002 ) plays a critical role in removing the barriers that otherwise stand in the way of forming these connections. Specifically, we assume that, under normal circumstances, information about temporal context enables the hippocampus to form distinct representations between study events that occurred far apart in time. The PFC has been suggested as an important source of temporal context information that feeds into the hippocampal system ( Polyn & Kahana, 2008 ), supporting the ability to keep different events happening at different points in time distinct from each other ( Howard & Kahana, 2002 ). Thus, when the PFC is deactivated during sleep, the result is a loss of this temporal context barrier keeping memories separated from each other, so the relevant connections across different episodes can more easily be made.

According to the current model, during initial encoding, scene-word associations were learned along drifting patterns of temporal contexts. In the critical condition, semantically-related words were separated by several blocks of other items, such that the temporal context pattern had changed significantly by the time the second association was studied. Thus, when subjects were cued with the scene and the first letter of the target word, the non-target word could not be retrieved along with the target word, so other irrelevant information retrieved created interference in the cortex. During sleep, we hypothesize that spontaneous activity in the brain caused reactivation of the word items, which then triggered hippocampal pattern completion to the associated scene. Critically, because the PFC temporal context was not present, the hippocampus was not pushed to keep separating its memories of different events that occurred at different times. Therefore, the reactivation of the associated scene and the existing synaptic connections between the semantically related words, which we hypothesize have been learned slowly over time in the cortex according to the CLS framework, would also trigger recall of the other word associated with that scene. Once reactivated, the previously distinct memories for the two word-scene events began to merge due to new learning on the co-activated neural activity patterns, causing the unpracticed word to benefit from the increased strength of the practiced word.

BDNF in Synaptic Plasticity and Memory

N.H. Woo , B. Lu , in Encyclopedia of Neuroscience , 2009

Learning, Memory, and Other Cognitive Functions

Given its central role in synaptic plasticity, numerous studies have examined how BDNF regulates the acquisition (learning) and retention (memory) of new information. Thus far, the strongest correlation is observed between BDNF and hippocampal-dependent forms of memory, which include declarative or episodic and spatial memory. During contextual learning, BDNF expression is rapidly and selectively upregulated in the hippocampus. When BDNF signaling is disrupted either by inhibitors or by genetic knockout, spatial learning is significantly impaired, as reflected by poor performance in the Morris water maze. In many cases impairments in memory were also mirrored with LTP deficits. For instance, deletion of BDNF or TrkB gene in the adult forebrain results in a significant attenuation of contextual fear or spatial memories, as well as hippocampal LTP.

A major advance came from a study on a SNP, which converts a valine (val) to a methionine (met) in the prodomain of the human BDNF gene. This SNP occurs with a frequency of approximately 19–25% in the Caucasian. Human subjects with the met allele exhibit lower hippocampal N -acetylaspartate (NAA), a putative measure of neuronal integrity and synaptic abundance. Functional imaging reveals an association of the met allele with abnormal hippocampal activation ( Figure 3(a) ). Most remarkably, subjects with the met-BDNF allele performed poorer in a hippocampal-dependent episodic memory task, but not in hippocampal-independent working memory and semantic memory tasks. In cultured neurons derived from rodent hippocampus, BDNF (val-BDNF) is packaged in secretory granules that are distributed as puncta throughout cell body and dendrites, with some localized at synapses. In contrast, significantly less met-BDNF-containing granules are localized to dendrites and synapses. Moreover, regulated secretion of met-BDNF induced by neuronal depolarization, but not constitutive secretion, is significantly reduced. Thus, impairments in trafficking, synaptic targeting, and/or regulated secretion may explain the specific memory deficits seen in human subjects with the met allele ( Figure 3(b) ). These results represent the first demonstration of a role for BDNF in human hippocampal function and of a single gene affecting human episodic memory.

research on memory retention

Figure 3 . Impact of a SNP in the BDNF gene on cognitive brain function and intracellular trafficking of BDNF. The SNP converts a valine to a methionine in amino acid 66 located in the prodomain of BDNF (val66met). (a) Differences in fMRI responses between val/val and val/met subjects during a memory task. Subjects with val/met genotype exhibit abnormal hippocampal activation (shown in red). The met/met subjects also exhibit deficits in hippocampus-dependent episodic memory. (b) Cellular phenotypes associated with the BDNF val66met SNP. Val-BDNF is distributed throughout a typical hippocampal neuron including distal dendrites and synapses. In contrast, met-BDNF is rarely localized at distal dendrites or synapses and fails to undergo depolarization-induced secretion. Failure of intracellular trafficking and activity-dependent secretion of BDNF may underlie the cognitive deficits observed in subjects with the met allele. (a) Reproduced from Egan MF, Kojima M, Callicott JH, et al. (2003) The BDNF val66met polymorphism affects activity-dependent secretion of BDNF and human memory and hippocampal function. Cell 112: 257–269, with permission from Elsevier.

Genetic analyses in mice show that genes affecting memory performance often impact other cognitive functions. It is now recognized that deficits in BDNF may contribute to neurological and psychiatric disorders. In studies of drug addiction, LTP and LTD have emerged as candidate mechanisms for drug-induced alterations in the nucleus accumbens and ventral tegmental area. Several reports have demonstrated that BDNF modulates behavioral sensitization to cocaine. Substantial evidence also points to its role in depression. There is reduced BDNF expression in the hippocampus of animal models for depression; chronic treatment with antidepressants increases its levels. However, it is unclear whether antidepressants achieve their clinical effects on depression by upregulation of BDNF.

Reconsolidation of Declarative Memory

María Eugenia Pedreira , in Memory Reconsolidation , 2013

10.5 Future insights using the declarative memory paradigm

Because our paradigm is well-established, it can be applied to different research questions. It is well-known that memory retention can be enhanced by pharmacological modulation or by real-life events in animal models if the treatment is applied during the labilization-reconsolidation process. By adapting our design and manipulating the properties of the reminder structure, another group in our laboratory investigated whether the same effects occur for human memories. They chose to modulate the acquired memory with a mild stressor, which has had an effect on memory processing in other paradigms ( Cahill, Gorski, & Le, 2003 ; Smeets, Otgaar, Candel, & Wolf, 2008 ), and to combine this stressor with different reminders. Thus, they presented a cue reminder (which reactivated the target memory) or a cue–response reminder (which only retrieved the original memory), and they applied a mild stressor after the presentation of the reminder (the cold pressor stress). Volunteers learned List 1, and 6 days later, one group reactivated their List 1 memory via exposure to the cue reminder prior to exposure to a mild stressor. Another group received a cue–response reminder before the stress treatment. Poor memory performance was found for the last group at testing on Day 7. Conversely, robust memory retention was shown at testing when the cold pressor stress administration was concurrent with the retrieved labile-memory state. These results revealed that a naturalistic, mild stressor could enhance reconsolidation, thus improving the long-term expression of a declarative memory ( Coccoz, Maldonado, & Delorenzi, 2011 ).

Our paradigm could also be used with pharmacological interventions. Future research may, for instance, study the impact of different drugs administered after reactivation given the relevance of different systems to the reconsolidation process.

Another potential use of our paradigm takes advantage of the fact that we have previously demonstrated the existence of memory strengthening. It would be useful to study the fate of a memory when the strengthening process is triggered and an amnesic agent is administered.

Finally, our promising results obtained from basic research do not answer a central question: Can real traumatic memories be changed? In this chapter, we revisited results indicating ways to demonstrate the reconsolidation process, modify the storage of declarative memories, characterize the boundary conditions of reconsolidation, and determine the function of reconsolidation. Incorporating these results, new treatments may be developed to specifically address traumatic or dysfunctional memories based on the features of memory reconsolidation.

Study shows stronger brain activity after writing on paper than on tablet or smartphone

Unique, complex information in analog methods likely gives brain more details to trigger memory.

A study of Japanese university students and recent graduates has revealed that writing on physical paper can lead to more brain activity when remembering the information an hour later. Researchers say that the unique, complex, spatial and tactile information associated with writing by hand on physical paper is likely what leads to improved memory.

"Actually, paper is more advanced and useful compared to electronic documents because paper contains more one-of-a-kind information for stronger memory recall," said Professor Kuniyoshi L. Sakai, a neuroscientist at the University of Tokyo and corresponding author of the research recently published in Frontiers in Behavioral Neuroscience . The research was completed with collaborators from the NTT Data Institute of Management Consulting.

Contrary to the popular belief that digital tools increase efficiency, volunteers who used paper completed the note-taking task about 25% faster than those who used digital tablets or smartphones.

Although volunteers wrote by hand both with pen and paper or stylus and digital tablet, researchers say paper notebooks contain more complex spatial information than digital paper. Physical paper allows for tangible permanence, irregular strokes, and uneven shape, like folded corners. In contrast, digital paper is uniform, has no fixed position when scrolling, and disappears when you close the app.

"Our take-home message is to use paper notebooks for information we need to learn or memorize," said Sakai.

In the study, a total of 48 volunteers read a fictional conversation between characters discussing their plans for two months in the near future, including 14 different class times, assignment due dates and personal appointments. Researchers performed pre-test analyses to ensure that the volunteers, all 18-29 years old and recruited from university campuses or NTT offices, were equally sorted into three groups based on memory skills, personal preference for digital or analog methods, gender, age and other aspects.

Volunteers then recorded the fictional schedule using a paper datebook and pen, a calendar app on a digital tablet and a stylus, or a calendar app on a large smartphone and a touch-screen keyboard. There was no time limit and volunteers were asked to record the fictional events in the same way as they would for their real-life schedules, without spending extra time to memorize the schedule.

After one hour, including a break and an interference task to distract them from thinking about the calendar, volunteers answered a range of simple (When is the assignment due?) and complex (Which is the earlier due date for the assignments?) multiple choice questions to test their memory of the schedule. While they completed the test, volunteers were inside a magnetic resonance imaging (MRI) scanner, which measures blood flow around the brain. This is a technique called functional MRI (fMRI), and increased blood flow observed in a specific region of the brain is a sign of increased neuronal activity in that area.

Participants who used a paper datebook filled in the calendar within about 11 minutes. Tablet users took 14 minutes and smartphone users took about 16 minutes. Volunteers who used analog methods in their personal life were just as slow at using the devices as volunteers who regularly use digital tools, so researchers are confident that the difference in speed was related to memorization or associated encoding in the brain, not just differences in the habitual use of the tools.

Volunteers who used analog methods scored better than other volunteers only on simple test questions. However, researchers say that the brain activation data revealed significant differences.

Volunteers who used paper had more brain activity in areas associated with language, imaginary visualization, and in the hippocampus -- an area known to be important for memory and navigation. Researchers say that the activation of the hippocampus indicates that analog methods contain richer spatial details that can be recalled and navigated in the mind's eye.

"Digital tools have uniform scrolling up and down and standardized arrangement of text and picture size, like on a webpage. But if you remember a physical textbook printed on paper, you can close your eyes and visualize the photo one-third of the way down on the left-side page, as well as the notes you added in the bottom margin," Sakai explained.

Researchers say that personalizing digital documents by highlighting, underlining, circling, drawing arrows, handwriting color-coded notes in the margins, adding virtual sticky notes, or other types of unique mark-ups can mimic analog-style spatial enrichment that may enhance memory.

Although they have no data from younger volunteers, researchers suspect that the difference in brain activation between analog and digital methods is likely to be stronger in younger people.

"High school students' brains are still developing and are so much more sensitive than adult brains," said Sakai.

Although the current research focused on learning and memorization, the researchers encourage using paper for creative pursuits as well.

"It is reasonable that one's creativity will likely become more fruitful if prior knowledge is stored with stronger learning and more precisely retrieved from memory. For art, composing music, or other creative works, I would emphasize the use of paper instead of digital methods," said Sakai.

Materials provided by University of Tokyo . Note: Content may be edited for style and length.

Journal Reference :

Keita Umejima, Takuya Ibaraki, Takahiro Yamazaki, Kuniyoshi L. Sakai. Paper Notebooks vs. Mobile Devices: Brain Activation Differences During Memory Retrieval . Frontiers in Behavioral Neuroscience , 2021; 15 DOI: 10.3389/fnbeh.2021.634158

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Study provides new insights into the process of memory recall

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An unforgettable time at a restaurant is not just about the food. The odors, the decor, the sound of the band playing, the conversations, and many other features may combine to form a distinctive memory of the night. Later, reviving any one of these impressions alone may be sufficient to bring back the entire experience.

A new study now reveals that in the brain, a complex memory similarly consists of a whole and its parts. The researchers found that while the overall experience is stored in the hippocampus, the brain structure long considered the seat of memory, the individual details are parsed and stored elsewhere, in the prefrontal cortex. This separation ensures that, in the future, exposure to any individual cue is sufficient to activate the prefrontal cortex, which then accesses the hippocampus for recall of the whole memory.

The findings, published July 13 in Nature , illuminate the distributed nature of memory processing in the brain, and provide new insights into the process of memory recall, which is less understood than memory storage.

It has been challenging to study memory as a distributed brain process, in part due to technical limitations. Dr. Priya Rajasethupathy, a neuroscientist at The Rockefeller University and her colleagues developed novel techniques to simultaneously record and manipulate neural activity from multiple brain areas as mice navigated multisensory experiences, encountering various sights, sounds and smells while in an endless corridor in virtual reality. The researchers trained the mice to associate different rooms, which were composed of different combinations of the sensory cues, as rewarding or aversive experiences. Later on, nudged by a specific scent or sound, the mice were able to recall the broader experience, and knew whether to happily expect sugar water or look out for an annoying puff of air.

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The experiments demonstrated that while the entorhinal-hippocampal pathway, a well-studied circuit involving the hippocampus and its surrounding region, was essential for forming and storing the experiences, the individual sensory features were being shipped off to prefrontal neurons. Later, when mice encountered particular sensory features, a different circuit was engaged. This time, the prefrontal neurons communicated with the hippocampus to conjure the relevant global memory.

" This suggests that there's a dedicated pathway for memory recall, separate from memory formation ," said Nakul Yadav, the study's first author and a graduate student in the Weill Cornell Graduate School of Medical Sciences Physiology, Biophysics and System Biology Program who is co-mentored by Dr. Rajasethupathy and by Dr. Conor Liston, associate professor of psychiatry and of neuroscience in the Feil Family Brain and Mind Research Institute at Weill Cornell Medicine.

These findings have implications for treatment of conditions such as Alzheimer's disease, where the deficits are thought to be more related to memory recall than storage. The existence of separate storage and retrieval pathways in the brain suggests that targeting of prefrontal recall pathways may be more therapeutically promising, Dr. Rajasethupathy said.

Dr. James Niemeyer, a postdoctoral associate in the Department of Neurological Surgery, and Dr. Jonathan Victor, the Fred Plum Professor of Neurology and professor of neuroscience in the Brain and Mind Research Institute, both at Weill Cornell Medicine, also contributed to the study.

Weill Cornell Medicine

Yadav, N., et al. (2022) Prefrontal feature representations drive memory recall. Nature. doi.org/10.1038/s41586-022-04936-2 .

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Wakeful resting and memory retention: a study with healthy older and younger adults

Cognitive Processing volume 20 , pages 125–131 ( 2019 ) Cite this article

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Studies indicate that a brief period of wakeful rest after learning supports memory retention, whereas distraction weakens it. It is open for investigation whether advanced age has a significant effect on the impact of post-learning wakeful rest on memory retention for verbal information when compared to a cognitively demanding distraction task. In this study, we examined (1) whether post-learning rest promotes verbal memory retention in younger and older adults and (2) whether the magnitude of the rest benefit changes with increasing age. Younger adults and older adults learned and immediately recalled two consecutive word lists. After one word list, participants rested wakefully for 8 min; after the other list, they solved matrices. Memory performance was again tested in a surprise free recall test at the end of the experimental session. We found that, overall, younger adults outperformed older adults. Also, memory retention was higher following a wakeful rest phase compared to distraction. A detailed analysis revealed that this wakeful rest benefit was significant for the older adults group, whereas the younger adults group retained a similar amount of information in both post-encoding conditions. We assume that older adults can profit more from a wakeful rest phase after learning and are more prone to distraction than younger adults. With increasing age, a short break immediately after information uptake may help better retain the previously learned information, while distraction after learning tends to weaken memory retention.

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The period between learning and recall critically affects memory performance (Müller and Pilzecker 1900 ). Evidence exists that a brief period of rest after learning leads to lower forgetting rates than working on a task (Alber et al. 2014 ; Cowan et al. 2004 ; Craig et al. 2015 ; Dewar et al. 2007 ; Mercer 2015 ). Recent findings indicated that post-encoding distraction has a detrimental effect on subsequent memory performance regardless of whether distractors are similar or dissimilar to the learning content (Dewar et al. 2012a , b ). In other words, forgetting can be induced by any mentally effortful post-encoding distraction task, irrespective of its content (Dewar et al. 2007 ). This view is supported by studies in different populations (amnesics: Alber et al. 2014 ; healthy older adults: Dewar et al. 2012a , b ; Alzheimer’s disease patients: Dewar et al. 2012a , b ; healthy younger adults: Mercer 2015 ; children: Martini et al. 2018 ), with different learning materials (visuo-spatial: Craig et al. 2015 ; verbal: Dewar et al. 2012a , b ) and post-encoding distraction tasks (games: Brokaw et al. 2016 ; perceptual spot-the-difference: Dewar et al. 2012a , b ; vocabulary learning: Mercer 2015 ). Moreover, various post-encoding interventions have shown a negative effect on memory retention (transcranial magnetic stimulation, or blocking of protein synthesis; see McGaugh 2015 ).

It has been suggested that memories take time to get consolidated, i.e. transferred into long-term memories, becoming less prone to distraction (Robertson 2012 ). It is assumed that memories are susceptible to interference immediately after acquisition (Wixted 2004 ). Consequently, reducing interference and providing a wakeful rest period should support memory consolidation and retention. However, recent studies with healthy younger adults indicated that post-encoding resting does not necessarily lead to higher delayed memory performances (Varma et al. 2017 ) and that memory retention is affected by the post-encoding phase only under certain conditions. It has been, for example, shown that rich autobiographical retrieval/future imagination after learning has a detrimental effect on the consolidation of recently acquired episodic memories (Craig et al. 2014 ). It has also been shown that wakeful resting after learning has an effect on direct forgetting (Schlichting and Bäuml 2017 ).

Most central for the current study, knowledge about the impact of a brief period of post-encoding rest in contrast to distraction across different age groups is scarce. Our brain changes across the lifespan and these changes often coincide with age-related alterations in cognitive task performance (Dennis and Cabeza 2008 ). For instance, with increasing age, grey matter and white matter losses in prefrontal cortex, parietal lobes, and specific parts of the medial temporal lobes are found to be related to a decrease in episodic memory, reasoning, working memory, and processing speed (e.g. Rodrigue and Raz 2004 ; Persson et al. 2006 ; Stebbins et al. 2002 ; Grady et al. 1998 ). Additionally, studies directly comparing older adults to younger adults point to complex patterns of hyper- and hypoactivation in different task-related brain regions (e.g. occipital cortex and prefrontal cortex, Grady et al. 1994 ), alterations in neural network switching (e.g. between a task-related brain state and a resting-related brain state; Pinal et al. 2015 ), and changes in neurotransmitter release (e.g. striatal dopamine; e.g. Bäckman et al. 2000 ) in older age.

Against this background, it is of relevance to test age-dependent differences in the effect of post-encoding rest on memory retention. Regarding this, first evidence was found by Craig et al. ( 2016 ) who investigated younger and older adults with a virtual route learning task. They showed that, while pointing accuracy was lower in older adults than in younger adults, both age groups significantly profited from a 10-min wakeful rest period compared to distraction (for fMRI data with an object–location association memory task see Kukolja et al. 2016 ).

The outline above indicates that (1) results in healthy younger adults diverge due to task manipulations and that (2) knowledge about cross-age differences in the magnitude of post-encoding rest benefits, compared to distraction, is scarce (Craig et al. 2016 ; Kukolja et al. 2016 ). Accordingly, the present study aimed at investigating (1) whether an 8-min post-encoding rest period, compared to a distraction condition, promotes verbal memory retention in younger and older adults and (2) whether the magnitude of post-encoding rest benefit, compared to distraction, changes with increasing age. In our study, healthy younger and older adults had to retain and immediately recall two word lists. We used a crossover design with participants being randomly assigned to two presentation orders. The first group (order 1) was required to wakefully rest for 8 min after having learned a first word list (rest condition), and to solve matrices for 8 min after having learned a second word list (distraction condition). The second group (order 2) received the exact opposite condition order, i.e. first distraction and then rest. Both groups were presented with a surprise delayed free recall test at the end of the experimental session. We hypothesised to find (1) lower retention rates in older adults, and (2) that resting, compared to distraction, would lead to higher retention rates in both age groups. We also expected that active rehearsal after encoding would lead to a better recall in all conditions and in both age groups.

Participants

Fifty older participants and forty younger participants were tested. Both younger and older participants were recruited from the same socio-economic background from acquaintances or through advertisement. Some older participants were recruited in cooperation with a local association that offers a variety of different activities (e.g. physical and cognitive training) and events for adults of 60 years of age and over. Inclusion criteria were no prior neurological, medical, or psychiatric conditions which may affect cognition as indicated by an informal interview and an education level of at least obligatory school (min. 8 years). Prior to the experimental session, older participants responded to the Mini-Mental State Examination (MMSE) to screen for cognitive impairment (cut-off = 27). Eighteen older participants were excluded from analyses as they performed under the MMSE cut-off score of 27. The final sample consisted of thirty-two older participants (25 females, age: M = 69.41 years, SD = 5.94, age range = 57–80 years; MMSE: M = 28.41, SD = 1.10). Participants in the younger group consisted of forty university students (32 females, age: M = 21.02 years, SD = 2.28, age range = 18–29 years). Groups were comparable in terms of gender distribution, χ 2 ( df = 1, N = 72) = .00, p = 1. Footnote 1

Materials and procedure

Figure 1 A illustrates the basic experimental procedure (Brokaw et al. 2016 ; Dewar et al. 2012a , b ; Varma et al. 2017 ). Participants were required to (1) retain a first word list; (2) immediately recall the words of this list; (3) perform an 8-min post-encoding condition, where they either rested wakefully or completed a distraction task; (4) retain a second word list; (5) immediately recall words of this second list; (6) perform either a distraction task or a rest condition; and (7) finally complete a surprise free recall test. In sum, all participants performed two learning tasks, one followed by rest, the other followed by distraction. Order of word lists and post-encoding conditions (rest and distraction) was counterbalanced within both age groups.

A Schematic illustration of the experimental design (order 1: first rest condition and then distraction condition). Conditions were counterbalanced (*). ~ 15 to 30 represents the temporal interval from immediate recall to delayed recall (first list: ca. 30 min; second list: ca. 15 min). For details see text. B Retention rates in rest and distraction conditions for older and younger adults. Error bars depict standard errors of the mean

Each word list consisted of 15 semantically unrelated German nouns and was taken from the verbal learning and memory test (Helmstaedter et al. 2001 ). Words were presented once, sequentially in the middle of the computer screen (Times New Roman, 100, black characters against a white background). Duration of stimulus presentation and interstimulus interval (blank white screen) were age specifically varied (older: 1000 ms/word, 1500 ms interstimulus interval; younger: 500 ms/word, 750 ms interstimulus interval). Word list presentation started after the question “Ready?” which was also displayed on the screen. An image of a writing hand presented in the middle of the computer screen indicated that participants should recall words in any order they wanted. Participants noted words on a white sheet of paper (one for each word list). Recall time was limited (older: 90 s; younger: 60 s). Footnote 2 After the immediate recall, participants either rested wakefully or solved matrices (distraction condition).

During the rest condition, participants were asked to relax quietly with their eyes closed in the darkened testing room. The experimenter did not leave and also rested. During the distraction condition, participants were required to solve matrices (older: standard progressive matrices, Raven 1958 ; younger: advanced progressive matrices, Set II, Raven et al. 1998 ). The matrices measure abstract reasoning. Participants are presented with several items of geometric patterns. Each item consists of a target pattern with a missing part in the bottom right corner. Participants have to select the missing part out of several alternatives. All participants were instructed to solve as many items as possible. The main reason for applying the matrices was that mental resources are continuously bound by the progressive character of the task, in addition to the ease to understand explanations. At the same time, matrices are visuo-spatial in nature, thus minimising interference with the previously learned word lists. Following each post-encoding condition, participants were asked to answer two questions: (1) “How often did you think about the words?” and (2) “How often did you consciously rehearse the previously learned words?”. Participants could answer by selecting one of 7 alternatives (from 1 = “not at all” to 7 = “very often”). All participants went through a probe phase prior to the main experiment where stimulus presentation and recall were trained with five words (which were semantically unrelated to the word lists of the main experiment). At the end of the experimental session, a surprise free recall test took place. Participants were asked to write down as many words from both lists as possible in any order they wanted. Recall time was limited (older: 180 s; younger: 120 s). The recall phase was followed by the question “Did you expect a surprise recall test at the end of the experimental session?”.

We compared age groups with regard to retention rates in the rest and distraction conditions (Fig. 1 B). We calculated for each word list a retention rate by dividing the number of words recalled during delayed recall by the number of words recalled during the immediate recall, separately for the rest and distraction conditions.

We conducted a mixed ANOVA on these retention rates with post-encoding condition (rest and distraction) as within-subject factor, and order (order 1: first rest condition and then distraction condition; order 2: first distraction condition and then rest condition) and age group (older and younger) as between-subjects factors. Results indicated a significant main effect of age group, F (1, 68) = 7.95, p < .006, \(\eta^{2}_{p}\) = .11, with older adults ( M = 57.05%, SD = 21.90) obtaining overall lower retention rates than younger adults ( M = 70.52%, SD = 18.76). The main effect of condition was also significant, F (1, 68) = 6.05, p < .016, \(\eta^{2}_{p}\) = .08, as were the interaction between condition and order, F (1, 68) = 4.61, p < .035, and the interaction between condition and age group, F (1, 68) = 7.74, p = .007. Other results were not significant, p’s > .1. Overall, retention rates for items followed by the rest condition ( M = 69.02%, SD = 24.70) were higher than retention rates for items followed by the distraction condition ( M = 60.05%, SD = 29.77). An investigation of the significant condition*order interaction by means of post hoc contrasts indicated that this difference (retention rates: rest condition > distraction condition) was significant for participants performing order 2 (i.e. first distraction condition and then rest condition), F (1,37) = 8.33, p = .006, \(\eta^{2}_{p}\) = .18, but not for participants performing order 1, p > .1 (i.e. first rest condition and then distraction condition; see Table 1 ). We also carried out an investigation of the significant condition*age group interaction by means of post hoc contrasts. Results indicated that the difference between conditions (retention rates: rest condition > distraction condition) was significant for the older participants, F (1,31) = 10.05, p = .003, \(\eta^{2}_{p}\) = .25, while younger participants performed comparably accurately in both conditions, p’s > .1 (see Fig. 1 B).

A further mixed ANOVA with post-encoding condition (rest and distraction) as within-subject factor, and order (order 1 and order 2) and age group (older and younger) as between-subjects factors was performed separately on scores given to question 1 (“How often did you think about the learned words?”) and scores given to question 2 (“How often did you consciously rehearse the learned words?”). Results of the analysis carried out for question 1 indicated a significant main effect of age group, F (1, 68) = 14.39, p < .001, \(\eta^{2}_{p}\) = .17, and a significant main effect of condition, F (1, 68) = 58.29, p < .001, \(\eta^{2}_{p}\) = .46. Other results were not significant, p’s > .1. Similar results were found in the analysis performed for question 2. The main effect of age group, F (1, 68) = 42.49, p < .001, \(\eta^{2}_{p}\) = .20, and the main effect of condition, F (1, 68) = 37.29, p < .001, \(\eta^{2}_{p}\) = .35, were significant. Other results were not significant, p’s > .1. In sum, older adults reported having thought and rehearsed the words more often than younger adults. Overall, people reported having thought and rehearsed the words more often following the rest condition than following the distraction condition (see Table 2 ).

Finally, we performed a Spearman rank-order correlation analysis for the two age groups separately between retention rates and scores obtained in questions 1 and 2. This analysis was carried out for the rest and distraction conditions separately. Results were not significant, p’s > .1, indicating no relation between memory performance and retention strategies. Correlations were also not significant when groups were collapsed.

Fifteen older adults and eleven younger adults indicated that they had expected a surprise free recall test, χ 2 ( df = 1, N = 72) = 2.89, p = .089. Independent t -tests showed no significant differences in retention rates between participants expecting a surprise free recall test and those who did not, p’s ≥ .10.

The present study aimed at investigating age-related differences in the impact of a brief period of rest after learning, in contrast to distraction (here, solving matrices), on the recall of verbal memory material. Overall, older adults retained fewer words than younger adults, indicating a memory decline with increasing age (Li 2002 ; Sander et al. 2012 ). Importantly, post-encoding rest and distraction differently affected memory retention in the two age groups. While older adults and younger adults performed comparably in the rest condition, distraction after learning significantly affected memory retention of the older participants. These novel findings support previous studies with healthy older adults. For instance, Dewar et al. ( 2012a , b ) found that verbal memory retention over 7 days was better when the immediate recall was followed by 10 min of wakeful rest than when it was followed by a spot-the-difference task. Our results partially support the findings of Craig et al. ( 2016 ), who investigated the impact of a brief period of wakeful rest across younger and older adults with a visuo-spatial learning task. They found that pointing accuracy in a virtual spatial navigation task was affected by a post-encoding perceptual spot-the-difference game when compared to a wakeful rest condition in both older and younger adults. In our study, we found a detrimental effect of distraction in the older group, but not in the younger group. Findings of studies with healthy younger adults are inconsistent—while some studies reported a supportive effect of post-encoding rest (Brokaw et al. 2016 ; Mercer 2015 ), others reported no beneficial effect of rest on memory retention (Varma et al. 2017 ; Martini et al. 2017 ). The latter studies indicate that wakeful rest might not be a necessary prerequisite for episodic memory consolidation. For instance, in a study with healthy young adults, Varma et al. ( 2017 ) found that memory retention was not affected by a distractor (n-back) task even when the complexity of the task was increased. Varma et al. ( 2017 ) assumed that post-encoding cognitive engagement probably has no interfering effect when the task has minimal demands on semantic processing and episodic memory supported by the hippocampus. Accordingly, memory consolidation can take place in parallel to task processing. In our study, participants solved matrices which have been related to hippocampus functioning (Colom et al. 2013 ; Zhu et al. 2017 ). Based on the assumption that hippocampal structures are relevant to post-encoding memory consolidation (Dewar et al. 2007 ) and that answering matrices involves the hippocampus, we should have found a detrimental effect of the distraction condition on memory retention in both age groups. However, this was only found in older adults, while younger adults were unaffected by the distraction task. One possible explanation for our results is that younger participants built memory representations of higher strength/quality than older adults, and were consequently less prone to distraction (de Zubicaray et al. 2011 ; McGaugh 2015 ; Paller and Wagner 2002 ; Robertson 2012 ; Wixted 2004 ). Indeed, the ability to retain newly acquired information seems to decrease with increasing age, accompanied by hypoactivation in memory-relevant brain regions, compensatory hyperactivation of brain areas relevant to attention and executive control, and reductions in interregional connectivity (see, for example, Lindenberger 2014 ; Park and Reuter-Lorenz 2009 ). As our results indicate, this decrease in retaining newly acquired information after distraction is particularly pronounced in older participants. Though deficient/slow consolidation of memory contents is a reasonable explanation for age-related memory declines after distraction, alternative hypotheses have to be taken into account. As repeatedly shown in different domains (Hinault et al. 2017 ; Lemaire 2016 ), younger and older individuals differ from each other in the development, choice, and application of cognitive strategies. Older adults are less efficient and less flexible in strategic choice than younger adults due to an age-related decline of frontal lobe functioning (Daselaar and Cabeza 2013 ). Memory contents supported by efficient memory strategies (e.g. visual imagination, stories) may be more resistant to interference in the distraction condition, while strategies may be less relevant in the rest condition. Since younger individuals are more efficient in developing and applying strategies, they should experience a smaller decline in long-term retention after distraction than older adults. In the present study, we did not investigate memory strategies. Future research may elucidate the effect of individual strategies in memory acquisition and memory retention on the benefit associated with a rest condition.

Finally, we found that resting supported memory retention in older adults only for the second word list. These results indicate that 8-min resting after encoding word list 1 (rest condition) was not sufficient to shield memory representations against interference through the following learning unit (distraction condition). Thus, in older adults, position and length of the resting phase seem to be of relevance. Our findings cannot be directly compared with other studies using a within-subject design (e.g. Dewar et al. 2012a , b ) as, even though post-encoding conditions were counterbalanced, order effects were not explicitly reported.

We should acknowledge some limitations. First, we tested almost four times more female participants than male participants. We cannot, therefore, make any conclusion about possible gender differences in memory retention after wakeful rest and distraction. Second, our study leaves open whether the education level might have been a modulating factor. Previous studies have found that higher education is positively related to cognitive functioning throughout adulthood and is negatively associated with the risk of dementia (e.g. Anstey and Christensen 2000 ; Hall et al. 2007 ). However, there has been also evidence that participants with lower education engaging frequently in cognitive activities show significant compensatory benefits for episodic memory (Lachman et al. 2010 ). Finally, we did not administer other cognitive tasks. Therefore, the association between wakeful rest benefit and other cognitive abilities such as interference inhibition, working memory, or divided attention remains to be investigated. Future studies might also consider the effects of encoding strategies, the complexity of the learning material, the length of the learning phase, and the specific time point of resting on memory retention across the lifespan.

To conclude, we found that older adults profited from a brief period of wakeful rest and were more affected by post-encoding distraction than younger adults. Our results suggest that a brief period of wakeful rest can support memory retention and that this strategy is especially effective in older age. This could be taken into account when planning cognitive interventions and counselling for older adults with a special focus on memory.

We performed a mixed ANOVA with post-encoding condition, order, and gender as factors to consider possible gender effects on retention rates. Age was not entered into this analysis as the two age groups had a comparable gender distribution. We found a significant condition*gender interaction, but no other significant results. Post hoc contrasts indicated that female participants profited significantly from the rest condition relative to the distraction condition, whereas male participants did not show any difference between conditions. These results should be considered with caution as the sample size of the two gender groups differed relevantly (57 females vs. 15 males).

In a pilot study, we tested immediate memory performance of 5 younger adults under different stimulus durations and interstimulus intervals. According to the results of this pilot study and the results by Ecker et al. ( 2015 ), we decided to use a stimulus presentation duration of 500 ms and an interstimulus interval of 750 ms. Based on the existing findings that psychomotor speed is reduced in elderly people (e.g. Salthouse 1991 , 1994 ), we decided to double the stimulus and interstimulus presentation durations for the older group. Moreover, older participants received 30 s more time than younger participants to recall words for a better comparability of memory performance between the two age groups.

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Acknowledgements

Open access funding provided by University of Innsbruck and Medical University of Innsbruck. We gratefully acknowledge Florian Sutter’s work on data collection and management for this study.

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Markus Martini, Pierre Sachse & Caroline Martini

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Laura Zamarian & Margarete Delazer

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Martini, M., Zamarian, L., Sachse, P. et al. Wakeful resting and memory retention: a study with healthy older and younger adults. Cogn Process 20 , 125–131 (2019). https://doi.org/10.1007/s10339-018-0891-4

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Received : 27 June 2018

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Published : 30 October 2018

Issue Date : 01 February 2019

DOI : https://doi.org/10.1007/s10339-018-0891-4

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The Impact of Sleep on Learning and Memory

By Kelly Cappello, B.A.

For many students, staying awake all night to study is common practice. According to Medical News Today , around 20 percent of students pull all-nighters at least once a month, and about 35 percent stay up past three in the morning once or more weekly.

That being said, staying up all night to study is one of the worst things students can do for their grades. In October of 2019, two MIT professors found a correlation between sleep and test scores : The less students slept during the semester, the worse their scores.

So, why is it that sleep is so important for test scores? While the answer seems simple, that students simply perform better when they’re not mentally or physically tired, the truth may be far more complicated and interesting.

In the last 20 years, scientists have found that sleep impacts more than just students’ ability to perform well; it improves their ability to learn, memorize, retain, recall, and use their new knowledge to solve problems creatively. All of which contribute to better test scores.

Let’s take a look at some of the most interesting research regarding the impact of sleep on learning and memory.

How does sleep improve the ability to learn?

When learning facts and information, most of what we learn is temporarily stored in a region of the brain called the hippocampus. Some scientists hypothesize that , like most storage centers, the hippocampus has limited storage capacity. This means, if the hippocampus is full, and we try to learn more information, we won’t be able to.

Fortunately, many scientists also hypothesize that sleep, particularly Stages 2 and 3 sleep, plays a role in replenishing our ability to learn. In one study, a group of 44 participants underwent two rigorous sessions of learning, once at noon and again at 6:00 PM. Half of the group was allowed to nap between sessions, while the other half took part in standard activities. The researchers found that the group that napped between learning sessions learned just as easily at 6:00 PM as they did at noon. The group that didn’t nap, however, experienced a significant decrease in learning ability [1].

How does sleep improve the ability to recall information?

Humans have known about the benefits of sleep for memory recall for thousands of years. In fact, the first record of this revelation is from the first century AD. Rhetorician Quintilian stated, “It is a curious fact, of which the reason is not obvious, that the interval of a single night will greatly increase the strength of the memory.”

In the last century, scientists have tested this theory many times, often finding that sleep improves memory retention and recall by between 20 and 40 percent. Recent research has led scientists to hypothesize that Stage 3 (deep non-Rapid Eye Movement sleep, or Slow Wave Sleep) may be especially important for the improvement of memory retention and recall [2].

How does sleep improve long-term memory?

Scientists hypothesize that sleep also plays a major role in forming long-term memories. According to Matthew Walker, professor of neuroscience and psychology at UC Berkeley, MRI scans indicate that the slow brain waves of stage 3 sleep (deep NREM sleep) “serve as a courier service,” transporting memories from the hippocampus to other more permanent storage sites [3].

How does sleep improve the ability to solve problems creatively?

Many tests are designed to assess critical thinking and creative problem-solving skills. Recent research has led scientists to hypothesize that sleep, particularly REM sleep, plays a role in strengthening these skills. In one study, scientists tested the effect of REM sleep on the ability to solve anagram puzzles (word scrambles like “EOUSM” for “MOUSE”), an ability that requires strong creative thinking and problem-solving skills.

In the study, participants solved a couple of anagram puzzles before going to sleep in a sleep laboratory with electrodes placed on their heads. The subjects were woken up four times during the night to solve anagram puzzles, twice during NREM sleep and twice during REM sleep.

The researchers found that when participants were woken up during REM sleep, they could solve 15 to 35 percent more puzzles than they could when woken up from NREM sleep. They also performed 15 to 35 percent better than they did in the middle of the day [4]. It seems that REM sleep may play a major role in improving the ability to solve complex problems.

So, what’s the point?

Sleep research from the last 20 years indicates that sleep does more than simply give students the energy they need to study and perform well on tests. Sleep actually helps students learn, memorize, retain, recall, and use their new knowledge to come up with creative and innovative solutions.

It’s no surprise that the MIT study previously mentioned revealed no improvement in scores for those who only prioritized their sleep the night before a big test. In fact, the MIT researchers concluded that if students want to see an improvement in their test scores, they have to prioritize their sleep during the entire learning process. Staying up late to study just doesn’t pay off.

Interested in learning more about the impact of sleep on learning and memory? Check out this Student Sleep Guide .

Author Biography

Kelly Cappello graduated from East Stroudsburg University of Pennsylvania with a B.A. in Interdisciplinary Studies in 2015. She is now a writer, specialized in researching complex topics and writing about them in simple English. She currently writes for Recharge.Energy , a company dedicated to helping the public improve their sleep and improve their lives.

Posted on Dec 21, 2020 | Tagged: learning and memory

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Memory Retention

Memory retention refers to the ability to remember information over a period of time. In short, it is the process of retrieving information after it has been encoded and stored. Occasionally, our retention may decay, and our stored memory is lost as time goes on. This event is also known as forgetting. When we forget things, there is an error in the retrieval process that causes memory to be reconstructed. So what exactly are the factors that contribute to memory loss, and how can we improve our memory retention?

According to Hermann Ebbinghaus, our memory retention is characterized by the “Ebbinghaus Forgetting Curve”, which illustrates the rate at which we forget something after it is learned. Based on the graph, information is initially forgotten very quickly, but the rate of memory loss slows down as time goes on. However, it is important to note that memory decay can fluctuate based on the strength of your memory. For example, there are a number of techniques that can increase the durability of a memory. Many of these memorization methods are utilized in teaching and studying.

A few techniques most commonly used by students are the testing effect, spacing, and schemas. You have probably experienced the testing effect in school, which is the process of frequent testing to encourage active information recall. This allows you to become aware of what you have learned, as well as what you can revise in order to remember something better. The next method, spacing, is when you learn information over a prolonged period of time rather than cramming everything at once. Research has shown that breaking down topics into various segments improves the ability to retain memory. Another useful method is the application of schemas. Schemas are mental “shortcuts” that help you understand and classify new information.

Memorization is not only important when it comes to education, but it is also useful in everyday life. Practically all of our day-to-day actions are the accumulation of everything we have memorized ever since we were young. Additionally, expertise in any field requires thorough practice to prevent forgetting. Overall, while it may be challenging to take in new information, our ability to retain concepts allows us to continue learning as we grow.

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Picture Source: wonderfulengineering.com

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