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Spaced repetition strategy to enhance retention

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Spaced repetition strategy to enhance retention

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Spaced repetition is a strategy that can be used to help enhance retention. This strategy works by teaching a skill and then coming back to the skill in strategically increasing intervals. For instance, if we introduce vocabulary on Monday, we can have students do an activity with that vocabulary on Wednesday and then test them on it on Friday. This way we are recalling the information at different times throughout the week. This allows us to reduce the stress that we are putting on our working memory. By revisiting the skill we are forcing students to recall the material. This strengthens the neural connections. Then we can add to the connections. Doing this in small pieces will allow us to take the pressure off of working memory. According to an article, " Compared with restudying the material in immediate succession, interweaving repetitions with time or other materials benefits memory" (Feng, 2019). Making the neural connections stronger helps to retain the information that we are learning.

This strategy is beneficial as a social studies teacher because it will allow personalized learning to an extent. Using spaced repetition allows students who need more help to get that help and practice they need since we are not building on the skill until later on. This also allows advanced learners to use their skill in more complex situations without putting pressure on struggling students. This strategy allows us to focus on parts of the skill. This way we can master the first part before making it more complex. The spaced repetition strategy is also valuable when it comes to students who have experienced trauma. Using this type of strategy keeps the classroom structured. Students know that when we are learning a skill, we will revisit and practice before moving on to a more enhanced skill. This provides a classroom that is predictable and stable that allows students to feel safe and encouraged. Trauma can cause problems to the hippocampus decreasing its ability to function properly. By being strategic on when we are recalling skills, we can help strengthen these neuron connections, causing us to counteract memory deficits.

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Abstract

Spaced learning has been shown consistently to benefit memory compared with massed learning, yet the neural representations and processes underlying the spacing effect are still poorly understood. In particular, two influential models (i.e., the encoding variability hypothesis and the study-phase retrieval hypothesis) could both model behavioral performance very well, but they make opposite hypotheses regarding the spacing effect's neural mechanisms. The present study attempted to provide empirical neural evidence to adjudicate these competing hypotheses. Using spatiotemporal pattern similarity (STPS) analysis of EEG data, this study investigated whether and how repetition lags (massed/short-spaced/long-spaced) modulated the STPS's contribution to episodic memory encoding in male and female human participants. The results revealed that greater item-specific STPS in the right frontal electrodes at 543-727 ms after stimulus onset was associated with better memory performance. More importantly, this STPS was larger under the spaced-learning condition than the massed-learning condition and partially mediated the spacing effect on memory performance. In addition, we found that massed learning was associated with stronger repetition suppression in the N400 component that reflected momentary retrieval strength, but reduced activity in the late positive component that was associated with memory retrieval. These results suggest that spaced learning improves long-term memory by increasing retrieval effort and enhancing the pattern reinstatement of prior neural representations, which may be achieved by reducing the momentary retrieval strength as the extended repetition lags might help to eliminate the residual representation in working memory.

SIGNIFICANCE STATEMENT As one of the most ubiquitous and fundamental phenomena in the history of memory research, the spacing effect provides an important window into understanding how enduring memory is formed in the brain and how different practice strategies could modulate these mechanisms to affect memory performance. By leveraging the neural representational analysis on scalp EEG data, the current study provides the first empirical data to show that spaced learning enhances memory by improving the spatiotemporal similarity that occurs at a late time window. Our results support the study-phase retrieval hypothesis but not the encoding variability hypothesis and emphasize the role of neural pattern reinstatement in strengthening memory via repeated study.

Introduction

Among these models, the encoding variability hypothesis makes specific predictions regarding the representational mechanisms of the spacing effect. In particular, it assumes that greater variability across learning repetitions provides more routes to effective retrieval ( Estes, 1959). Due to contextual drift over time ( Glenberg, 1979), longer inter-repetition intervals (IRIs) would lead to greater contextual change and thus more variable encoding, resulting in better memory performance. Consistent with this hypothesis, introducing variations across massed repetitions has been found to improve memory performance ( Paivio et al., 1988; Appleton-Knapp et al., 2005). Nevertheless, variations under the spaced-learning condition are not beneficial and sometimes are even detrimental to memory performance ( Verkoeijen et al., 2004; Toppino and Gerbier, 2014).

Materials and Methods

Participants.

Thirty-three healthy Chinese college students (19 females; mean age = 19.8 ± 1.8 years, range 17-25) completed the experiment. All participants were right-handed, had a normal or corrected-to-normal vision, and no history of neurological or psychiatric diseases. Six additional subjects were recruited but excluded from final analysis due to their high rate of "no response" trials (one subject) or noisy EEG data (five subjects). The study was approved by the Institutional Review Board of the State Key Laboratory of Cognitive Neuroscience and Learning at Beijing Normal University.

Materials.

We used 288 pictures of Chinese faces in the learning phase and another 288 faces as foils in the recognition memory test. To minimize the primacy and recency effects, 12 additional faces (four for each run) were added at the beginning and the end of the study lists, but were not tested in the recognition task. All faces were unfamiliar to the subjects and were drawn from the CAS-PEAL face database ( Gao et al., 2008). They were presented in the same size (180 × 240 pixels) in the center of the computer screen on a gray background.

Procedure and design.

During the encoding phase, participants were asked to make an age judgment (older or younger than 30 years old) on each presented face by pressing one of two buttons. They were not told about the subsequent memory task. All faces were presented twice within a run, with one-third under the massed (MA) learning condition (i.e., the IRI was 0-1 trials), one-third under the short-spaced (SS) learning condition (i.e., the IRI was 4-8 trials), and the rest under the long-spaced (LS) learning condition (i.e., the IRI was 94-96 trials) ( Fig. 1 A). The materials used in the three conditions were fully counterbalanced across participants. Each trial started with a fixation cross lasting 500-800 ms (randomly jittered), followed by the presentation of the stimulus for 1000 ms and a blank screen for 2500 ms. Participants were allowed to respond within 2000 ms after stimulus onset. The encoding phase consisted of three runs, each lasting 10 min.

A recognition memory test was conducted after a 30 min visual change detection task that was used as a distractor. During the recognition phase, participants were asked to judge whether they had studied each face earlier on a 6-point scale, with 1 indicating "definitely new" and 6 indicating "definitely old." In total, 576 faces (288 old faces and 288 new faces) were pseudorandomly mixed and presented one by one over three runs, with the constraint that the numbers of old and new faces were matched within each run. Each trial started with a fixation cross lasting 500-800 ms (randomly jittered), followed by the presentation of the face for up to 3000 ms unless a response was made. A blank screen was then presented until the next trial ( Fig. 1 B). We used a fixed intertrial interval (ITI) of 4000 ms in the recognition phase so that the total duration of this phase was not affected by participants' response time, which could help to discourage the participants from making hasty responses.

Behavioral data analysis.

For memory performance, the old faces recognized with high confidence (scored 5 or above) were defined as remembered items and those scored 3 or below were defined as forgotten items. These cutoffs were chosen so that there were approximately equal numbers of remembered (42.59%) and forgotten items (40.82%). Both the hit rate and discriminability ( d′) were analyzed by condition. Response time (RT) and response consistency (i.e., the rate of same response between two repetitions) in the age judgment task during the encoding phase were analyzed by spacing condition (MA, SS, and LS), subsequent memory (remembered vs forgotten), and, for RT only, by repetition (first vs second presentation). Repeated-measures ANOVA and paired-sample t test were conducted to examine the spacing effect in the encoding task and memory performance.

EEG recording and preprocessing.

Participants were seated ∼100 cm away from the computer screen in a soundproof, light-adjustable room. Continuous EEG data were recorded with a sampling rate of 1024 Hz using the 64-channel ActiveTwo EEG system (Biosemi). Ag-AgCl electrodes were mounted according to the 10-20 system.

EEG data preprocessing was implemented using MATLAB-based toolbox Fieldtrip (RRID: SCR_004849) ( Oostenveld et al., 2011) and in-house MATLAB (RRID: SCR_001622) scripts. EEG data were re-referenced to the average of all electrodes, downsampled to the rate of 256 Hz, and filtered with a band-pass filter of 0.5-40 Hz. Eye movements, blinks, and muscle artifacts were identified and corrected using the independent components analysis algorithm. The continuous data were then segmented into epochs from −200 to 1000 ms with regard to stimulus onset. The prestimulus interval (−200 to 0 ms) was used as the baseline for baseline removal procedure. Trials contaminated by any remaining eye movement, blink, or muscle activity were rejected by visual inspection.

Spatiotemporal pattern similarity analysis (STPS).

We constructed spatiotemporal feature vectors from the single-trial epoch data and conducted the spatiotemporal pattern similarity analysis ( Lu et al., 2015). The spatial features were scalp voltages from one of the six regions for better spatial specificity (15 channels per region) ( Fig. 2 A), and the temporal features were selected using a 100 ms sliding window (26 time points) from the epoch data, with a step size of one time point. The similarity between trials was calculated using Pearson correlation. The correlation coefficients were then converted to Fisher's Z scores for subsequent statistical analysis.

The within-item (WI) STPS was obtained by calculating the similarity between two repetitions of the same item. To determine whether the WI similarity reflected item-specific representations or common cognitive processes, we calculated the similarity for between-item (BI) pairs that matched the WI pairs in terms of their memory performance, spacing condition, number of repetitions (one or two), and IRI. Specifically, for a WI pair, we selected a BI pair in which the two trials were from the same spacing condition and showed the same subsequent memory performance as those in the WI pair. In addition, one trial of the pair was the first repetition of an item and the other trial was the second repetition of another item. Finally, the ITI between BI pairs and WI pairs were matched as closely as possible, although we did not find a significant effect of ITI on the pattern similarity of BI pairs (χ = 0.21, p = 0.65). A greater WI similarity than BI similarity should reflect item-specific encoding.

In the current study, we aimed to examine the neural representational mechanisms underlying the spacing effect in enhancing episodic memory. As a result, we were particularly interested in the neural differences between spaced and massed condition that were associated with subsequent memory effect. In other words, although there could be many neural differences between the spaced and massed conditions, they might reflect different cognitive functions such as sensory processing or motor control, but such similarities are not theoretically (directly) related to the condition differences in memory performance. Instead, only the condition differences in neural pattern similarity that are linked to successful memory encoding would explain the spacing effect in memory. As a result, we needed first to localize the memory related neural patterns and then test whether spacing modulated them.

To achieve this goal, we did the following hypothesis-driven statistical analyses. First, we located the spatiotemporal windows where the item-specific representation was associated with subsequent memory performance. The WI STPS and BI STPS were separately grouped and averaged across pairs according to the status of subsequent memory performance and spacing condition, separately for each individual participant. Whole-brain three-way ANOVAs were conducted, with spacing condition (MA, SS, and LS), subsequent memory performance (remembered/forgotten), and item specificity (WIs/BIs) as within-subject factors. We particularly focused on the contrast of subsequent memory by item specificity interaction, which should be orthogonal to the spacing effect. In other words, although there were more remembered items in the spaced learning conditions than massed learning condition, the spacing effect should not bias the subsequent memory effect since the remembered and forgotten items were separately averaged within each learning condition before the three-way ANOVA. To make sure that the subsequent memory effect was consistent across spacing conditions, we also examined the subsequent memory by spacing interaction as well as the three-way interaction. Focusing on the time windows showing significant item-specific subsequent memory effects (i.e., significant item specificity by subsequent memory interaction, but no three-way interaction or memory by spacing interaction), we conducted post hoc comparisons to determine whether remembered items showed greater item-specific representations.

To determine the representational mechanisms of the spacing effect on subsequent memory, we then focused on those windows where the item-specific pattern similarity predicted subsequent memory performance to further investigate how spacing affected item-specific representations. In this analysis, the remembered items and forgotten items in each learning condition were pooled together and then averaged, which would allow for a better examination of the spacing effect than would separate averages of remembered and forgotten trials because there were different numbers of remembered items in different spacing conditions. We then conducted item specificity by spacing ANOVA. Focusing on the clusters showing item specificity by spacing interactions, we conducted post hoc comparisons to determine whether spacing could enhance item-specific pattern similarity.

Univariate event-related potential (ERP) analysis.

To detect the ERP components showing the subsequent memory effect, we averaged the EEG responses according to subsequent memory performance, spacing condition, and repetition separately for each individual participant. Similar to the STPS analysis, we first conducted a whole-brain three-way ANOVA, with spacing condition (MA, SS, and LS), subsequent memory performance (remembered/forgotten), and repetition (Rep1/Rep2) as within-subject factors. The ERP components associated with subsequent memory performance were defined as those showing a significant main effect of subsequent memory and no significant interaction with other two factors.

Focusing on these components, we then investigated whether spacing would modulate these components. Again, the remembered items and forgotten items in each learning condition were pooled together and then averaged. Since spacing could only modulate the EEG response evoked by the second repetition, we separately compared MA versus SS, MA versus LS, and SS versus LS on the ERP evoked by the second repetition. In an exploratory analysis, we also investigated whether spacing could modulate the repetition priming effect by a whole-brain spacing by repetition ANOVA.

Nonparametric cluster-based permutation test.

Corrections for multiple comparisons were performed using a nonparametric statistical method based on cluster-level permutation tests implemented in Fieldtrip toolbox. Statistical testing was performed for every time window, and the time windows whose statistical value was larger than a threshold ( p = 0.05) were selected and clustered into connected sets on the basis of temporal adjacency. The observed cluster-level statistics were calculated by taking the sum of the statistical values within a cluster. Then, condition labels were permuted 10,000 times based on their exchangeability, and the maximum cluster statistic over all six regions in each permutation was chosen to construct a distribution of the cluster-level statistics under the null hypothesis. The nonparametric statistical test was obtained by calculating the proportion of randomized test statistics that exceeded the observed cluster-level statistics. For the spacing effect, the permutation test was conducted within the pre-defined window showing subsequent memory effect. For univariate analyses on ERPs, the procedures were the same except that the cluster was set on the basis of temporal and spatial adjacency. When pairwise comparisons between the three spacing conditions were conducted, the cluster-level tests were further corrected for multiple comparisons using Bonferroni correction.

Mixed-effects model.

The mixed-effects model is useful for modeling the influence of predictors at multiple levels of variables simultaneously and for jointly modeling both discrete and continuous variables ( Gelman and Hill, 2006). It has been used in sophisticated fMRI designs ( Ward et al., 2013; Xiao et al., 2017) and single-trial ERP analyses ( Valente et al., 2014; Payne et al., 2015). In this study, the mixed-effects model was implemented with lme4 ( Bates et al., 2015) in R (RRID: SCR_001905). Participants were included as a random effect. We used the likelihood ratio test to compare the models (with vs without the predictor) to determine the effect of the predictor.

Controlling the effect of univariate ERP amplitude on STPS.

To determine whether our key findings of pattern similarity were due to differences in univariate amplitude, we constructed linear mixed-effects models to examine the subsequent memory effect and spacing effect, by including the amplitude as covariate. In the model for the subsequent memory effect, memory strength (1-6) was used as the dependent variable; the WI pattern similarity in the spatiotemporal clusters showing subsequent memory effect was used as the predictor, and the corresponding mean EEG amplitude was included as the confounding factor. In the model for the spacing effect, the mean WI pattern similarity was used as the dependent variable, the spacing condition was used as the predictor, and the mean EEG amplitude was included as the confounding factor. Participants were included as a random effect.

Mediation analysis.

We performed the mediation effect test to further investigate whether the spacing effect on memory was mediated by the WI pattern similarity, which was related to both spacing condition and subsequent memory performance. Mixed-effects models as implemented by lme4 ( Bates et al., 2015) in R (RRID: SCR_001905) were used to test the relationship between spacing and memory strength (1-6 confidence responses were used to index memory strength) ( Y = a ₁ + b ₁ X + ε ₁; (2) spacing and WI similarity ( M = a ₂ + b ₂ X + ε ₂); (3) spacing and memory strength with a mediator ( Y = a ₃ + b ₃ X + bM + ε ₃). In those equations, Y is the dependent variable, X is the predictor, and M is the mediator. The indirect effect was estimated as b ₂ × b. We used distribution-of-the-product method to compute the confidence interval (CI).

Discussion

More importantly, this memory-related STPS was larger under the spaced learning condition than the massed learning condition and it partially mediated the spacing effect on memory. This contradicts the encoding variability hypothesis that greater dissimilarity benefits memory. Given the temporal contextual drift ( Glenberg, 1979), how could spaced learning generate stronger pattern similarity than massed learning? According to the context maintenance and retrieval model ( Polyn et al., 2009; Siegel and Kahana, 2014), when the first presentation was retrieved, its context could be reinstated ( Manning et al., 2011). This context reinstatement could partially counteract the temporal context drift. Moreover, under the massed condition, the neural representation of the first learning experience might be still activated in the short-term memory system when the IRI is short ( Raaijmakers, 2003; Van Strien et al., 2007). According to the new theory of disuse ( Bjork and Bjork, 1992; Bjork, 1999), an item in memory can be characterized by two "strengths": Storage strength reflects how well an item is learned and retrieval strength represents how accessible an item can be via recall or recognition. Under the massed learning condition, items are readily accessible and the strong momentary retrieval strength would reduce the memory retrieval processes, resulting in less gain in storage strength ( Zhao et al., 2015).

Third, the LPC representation contained no item-specific information but was associated with memory performance. Moreover, the LPC showed reduced response with repetitions and its amplitude was associated with the strength of memory-related STPS. Previous studies have shown that LPC is related to recollection ( Rugg and Yonelinas, 2003; Rugg and Curran, 2007; Kappenman and Luck, 2012), in particular, the retrieval of complex information about prior events. These results suggest that the LPC is associated with general memory-retrieval processes that contribute to pattern reinstatement. Due to the stronger momentary retrieval strength under the massed condition, the requirement for memory retrieval of prior learning and the reactivation of prior memory trace are impaired.

The present study failed to replicate the detrimental effect of long IRI. Some behavioral studies found that the spacing effect could be effective in terms of days or months ( Cepeda et al., 2008). In addition, the optimal interval also varied in different learning tasks, different repetition intervals, and different retention lags ( Cepeda et al., 2006; Pashler et al., 2009; Toppino and Gerbier, 2014). In the current study, we tried the longest possible IRI with the constraint that trials from each condition were all within one session (to avoid any cross-session differences). It seems that this long-spaced interval might not be long enough to bend the curve. In addition, this design unavoidably results in an imbalance in the temporal distributions of repetitions under different spacing conditions, which might have some effect on memory performance. Indeed, we found a significant spacing (MA vs SS) by serial position interaction ( F(3,96) = 2.74, p = 0.048). There were significant primacy and recency effects under the MA learning condition; that is, trials studied in the first and last quartiles of a run were remembered better than those studied in the middle ( t₍₃₂₎ = 2.33, p = 0.026). However, no such effect was found in the SS condition ( t₍₃₂₎ = 0.86, p = 0.4). This suggests that the serial position and spacing might interact to affect memory performance, which could potentially confound the result. Future studies could use a multiday design to determine whether pattern similarity plays a role in the inverted U-shaped relationship of spacing and memory.

Although our results suggest that repetition priming might be a result of residual working memory representation, which might hinder later pattern reinstatement and impair memory, either behavioral repetition priming or N400 repetition suppression could not quantitatively predict memory performance. In fact, many existing studies investigating this issue have found mixed results ( Zhao et al., 2015). Due to the fact that the repetition priming is affected by the initial processing strength and the repetition lag, and in turn, would affect the retrieval of prior representation and further encoding, it would be difficult to establish a direct association between repetition priming and memory performance ( Xue et al., 2011). Future studies should further investigate the relationship among repetition priming, neural repetition suppression, pattern reinstatement, and episodic memory performance.

In conclusion, our study provides several lines of novel neural evidence to advance our understanding of the mechanisms of repeated studies in enhancing memory. Contrary to the encoding variability hypothesis, better memory was associated with greater STPS that occurred at a late time window and spacing enhanced memory by increasing the STPS. This pattern similarity in the late window might reflect the reinstatement of more abstract representation, which was reduced under the massed condition due to the strong momentary retrieval strength. These results highlight the complex interactions of multiple memory processes and representations in determining memory performance.

Footnotes

This work was sponsored by the National Science Foundation of China (NSFC Grants 31730038 and 31861143040), the 111 Project (Grant B07008), the NSFC and the German Research Foundation (DFG) joint project NSFC 61621136008/DFG TRR-169, and the Guangdong Pearl River Talents Plan Innovative and Entrepreneurial Team (Grant 2016ZT06S220).

The authors declare no competing financial interests.

Correspondence should be addressed to Gui Xue at gxue{at}bnu.edu.cn

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Spaced Learning Enhances Episodic Memory by Increasing Neural Pattern Similarity Across Repetitions

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Spaced learning has been shown consistently to benefit memory compared with massed learning, yet the neural representations and processes underlying the spacing effect are still poorly understood.

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The sensory memory stores the information we are taking in through our five senses. This memory only lasts for a couple of seconds unless moved to short term memory. Our sensory memory processes information quickly and filters the information that we feel is important. This information is then moved to short term memory and all other data is discarded.

Our sensory memory has a large capacity since it is taking in multiple pieces of information at the same time. Much of this information is lost within seconds. According to an article, "sensory memory only lasts for a few seconds before either moving to another memory storage area or disappearing so you can experience other senses" (Camina, 2017). Once information is proceed, our neurons start to send information through our brain. If this fades or stops, the memory is then lost. If the information is important, the neurons sends the information to the working memory where it goes to the correct part of the brain to be processed further.

Sensory memory is important when it comes to educational environments. It helps students to filter out the important information and can help with student attention. Students are always taking in information through different senses at one given time. This allows them to process the information they are taking in through what they are seeing and hearing. In professional settings, our sensory memory allows us to react to situations that are happening around us quickly. By filtering this information, we can then determine what is needed.

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Mind-Brain Group: Biology and Subjectivity in Philosophy and Contemporary Neuroscience, Institute for Culture and Society, University of Navarra, Pamplona, Spain

Department of Learning and Curriculum, Faculty of Education and Psychology, University of Navarra, Pamplona, Spain

This review aims to classify and clarify, from a neuroanatomical, neurophysiological, and psychological perspective, different memory models that are currently widespread in the literature as well as to describe their origins. We believe it is important to consider previous developments without which one cannot adequately understand the kinds of models that are now current in the scientific literature. This article intends to provide a comprehensive and rigorous overview for understanding and ordering the latest scientific advances related to this subject. The main forms of memory presented include sensory memory, short-term memory, and long-term memory. Information from the world around us is first stored by sensory memory, thus enabling the storage and future use of such information. Short-term memory (or memory) refers to information processed in a short period of time. Long-term memory allows us to store information for long periods of time, including information that can be retrieved consciously (explicit memory) or unconsciously (implicit memory).

Introduction

A life full of unconnected events, of errors that do not lead to any lessons and of emotions without the ability to remember them is no life at all. Memory is precisely the capacity that allows us to connect experiences, learn and make sense of our lives. In short, it allows us to build our story. The full range of this complex capacity's neuroanatomical, neurobiological, neurophysiological, and psychological mechanism remain unknown and it presents a challenge for psychologists and neuroscientists who try to explain it. This review attempts to provide a rigorous overview that permits anyone who wants to approach the latest scientific findings on memory to do so, as well as to understand them and properly order them. We will focus on neuroanatomical, neurophysiological, and psychological mechanisms of the different types of memory.

We believe it is important to consider previous developments without which one cannot adequately understand the classifications of memories and the kinds of memory models that are now current in the scientific literature.

The three major classifications of memory that the scientific community deals with today are as follows: sensory memory, short-term memory, and long-term memory. Information from the world around us begins to be stored by sensory memory, making it possible for this information to be accessible in the future. Short-term memory refers to the information processed by the individual in a short period of time. Working memory performs this processing. Long-term memory allows us to store information for long periods of time. This information may be retrieved consciously (explicit memory) or unconsciously (implicit memory).

As Squire (2004) points out, the first theoretical approaches relevant to current neuroscience come from the 19th century. These include Maine de Biran (1804/1929) ( Maine de Biran, 1929) who, at the beginning of the century, wrote of mechanical memory, sensitive memory, and representative memory. The philosopher James, and his book The Principles of Psychology ( James, 1890), is also especially worth highlighting. Therein, James distinguishes between primary and secondary memory, thereby referring to short- and long-term memory, respectively.

The importance of Pavlov (1927) and Fitts and Posner (1967) are especially noteworthy during the first two thirds of the 20th century. Pavlov's studies are related to a type of memory that later would be called associative memory. Meanwhile, Fitts and Posner's studies are considered the first model to explain procedural memory.

Prior to the 60's, most systematizations of memory distinguished a more mechanical type of memory related to the acquisition of skills, which is, in turn, related to activity of the intellect. Unlike what followed, debates in this period were mainly philosophical or based on psychological intuition ( Ribot, 1881; Korsafoff, 1890).

Beginning in the 1960s, a series of experimental studies on how the brain stores information emerged, using animals and amnesic patients. Within this decade, Milner, Atkinson, and Shiffrin were especially important researchers.

The experimental modern era arguably began when Milner (1962) demonstrated, with HM experiments, that a seriously ill patient could acquire a new skill (hand-eye coordination) without any memory of having encountered the task before. "While this finding showed that memory is not unitary, discussions at the time tended to set aside motor skills as a special case representing a less cognitive form of memory. The suggestion was that the rest of memory is of one piece and is dependent on medial temporal lobe structures" ( Squire and Wixted, 2016).

A few years later, Atkinson and Shiffrin (1968) proposed a modal model of memory that constitutes one of the most influential explanations for the existence of different components in the memory system. The importance of this model is such that it must be explained in the next section, but for now it should simply be mentioned that the modal model establishes the existence of short-term storage (ACP), which receives sensory information that is processed by sensory and data storehouses within long-term memory. This storage system can generate reasoning and new deductions from existing ones.

With this brief historical and conceptual introduction laid out, we intend to delve into different types of memory in order to present the models that the scientific community has most accepted thus far. In the last section, and before the glossary, we identify the likely directions for future research. Now we turn on to our main task, presenting an overview of the latest scientific findings on memory, classified according to different types and mechanisms.

Sensory Memory: Iconic Memory

"Sensory memory is the capacity for briefly retaining the large amounts of information that people encounter daily" ( Siegler and Alibali, 2005). There are three types of sensory memory: echoic memory, iconic memory, and haptic memory. Iconic memory retains information that is gathered through sight, echoic memory retains information gathered through auditory stimuli and haptic memory retains data acquired through touch.

Scientific research has focused mainly on iconic memory; information on echoic and haptic memory is comparatively scarce. Thus, taking into account the goals of this article and that it is aimed at a higher education audience, presenting iconic memory as a paradigm of sensory memory is sufficient for an introductory overview.

Iconic memory retains information from the sense of sight with an approximate duration of 1 s. This reservoir of information then passes to short-term vision memory (which is analogous, as we shall see shortly, to the visuospatial sketchpad with which working memory operates).

Di Lollo's model ( Di Lollo, 1980) is the most widely accepted model of iconic memory. Therein, he considered iconic memory a storehouse constituted by two components: the persistence of vision and information.

(a) Persistence of vision. Iconic memory corresponds to the pre-categorical representation image/visual that remains between 100 and 300 ms. It is sensitive to physical parameters, such that it depends on retinal photoreceptors (rods and cones). It also depends on various cells in the visual system and on retinal ganglion cells M (transition cells) and P (sustained cells). It concludes its representation in the primary visual cortex (V1) of the occipital lobes. "The occipital lobe is responsible for processing visual information" ( Kamel and Malik, 2014).

(b) Persistence of information. Iconic memory is a storehouse of information that lasts 800 ms and that represents a codified and already categorized version of the visual image. It plays the role of storehouse for post-categorical memory, which provides visual short-term memory with information to be consolidated. For this, it travels through the ventral route (V) (V1 → V2 → V5 → inferior temporal cortex).

Subsequent research on visual persistence from Coltheart ( Coltheart, 1983) and Sperling's studies ( Sperling, 1960) on the persistence of information led to the definition of three characteristics pertaining to iconic memory: a large capacity, a short duration, and a pre-categorical nature.

Sperling (1960) demonstrated this large capacity after presenting the results of his total and partial reports. The full report consisted in presenting a 3 × 3 or 3 × 4 matrix of alphanumeric characters for a short period of time to subjects and later asking them which characters they remembered. On the other hand, in the partial report, subjects were directed to remember the characters in a row specifically assigned to them in the instructions. The total report's results showed that subjects were only able to recall between 3 or 4 letters of the total number. However, in the partial report, subjects remembered around 75% of those that were asked. In extrapolating the partial report's data to the total, it follows that individuals could report 9 of the 12 letters contained in the instructions (80% of the total), thus demonstrating a large capacity.

Regarding short-term, Sperling interpreted the results of the partial report as due to the rapid decline of the visual sign and reaffirmed this short duration by obtaining a decrease in the number of letters reported by the subject in delaying the audio signal for choosing a row to remember in the presentation. Averbach and Coriell's experiments ( Averbach and Coriell, 1961) corroborated Sperling's conclusion; they presented a variety of letters for a certain period of time to the subject. After each letter, and in the same position, they showed a particular visual sign. The participant's task was to name the letter that occupied the position of the visual sign. When the visual sign appeared immediately after the letters, participants could correctly name the letter that occupied the position of the sign, however, as the presentation of the sign became more delayed, participant performance worsened. These results also show the rapid decline of visual information.

Finally, regarding its pre-categorical nature, Sperling considered the information contained in this storehouse as physical information that maintains the raw data that is not related to the meaning of stimuli. Subsequently, evidence has been obtained that this system is not entirely pre-categorical ( Loftus et al., 1992) since the task improves when the stimuli to remember are letters or numbers instead of meaningless forms.

Short-Term Memory

FIGURE 1. Atkinson and Shiffrin's modal model.

Despite the explanatory power of Atkinson and Shiffrin's model, there were a number of issues that this model could not resolve, causing criticism of it. For example, this model implies that the longer an item remains in memory, the more likely it is to be transferred to long-term storage. But studies like those of Tulving and Pearlstone (1966) and Craik and Watkins (1973) show that said relationship does not exist.

As an introduction, it can be argued that Craik and Lockhart (1972) understood memory not as a process through which information is deepened at higher levels until it becomes part of long-term memory, but rather as a system of storehouses. Despite an emphasis on information processing (instead of structure), they continued to accept the existence of short-term memory as independent from long-term memory. For their part, Baddeley and Hitch's proposal ( Baddeley and Hitch, 1974) contemplated a multi-component working memory instead of a storage unit in the short term.

Working Memory

"The term working memory refers to a brain system that provides temporary storage and manipulation of the information necessary for such complex cognitive tasks as language comprehension, learning and reasoning" ( Baddeley, 1992). At first (1974), they proposed the existence of three subsystems within the multi-storehouse model of short-term memory: the central executive, a phonological or articulatory loop and a visuospatial sketchpad.

In general, we can say that the central executive controls attention, "the phonological loop ensures retention of verbal information and the visuospatial sketchpad is responsible for storage visual and spatial information" ( Grigorenko et al., 2012). The latter two sub-memory systems are equivalent to verbal and visual short-term memory systems, respectively.

Wang and Bellugi (1994) presented a genetically based test that supports the functional and anatomical separation of Baddeley's model with phonological and visuospatial storehouses. They compared two genetic syndromes (Williams and Down) with different brain morphology. Williams syndrome patients, despite having widespread mental handicaps, preserve their language skills, while Down syndrome patients preserve more partial capacities, but have very limited language skills. It was therefore assumed that the former would be better at verbal tasks related to operative memory, and that the latter would be better at visuospatial tasks related to operative memory. As expected, subjects with Williams syndrome performed better at phonological tasks, while subjects with Down syndrome, in turn, performed better at spatial tasks.

Later, Baddeley (2000) included a fourth subsystem, the episodic buffer (Figure 2), which combines information from the different subsystems in a kind of temporal representation.

FIGURE 2. Baddeley's model.

Here we will focus on the different subsystems that make up Baddeley's multi-storehouse model (2000), i.e., the central executive, the phonological or articulatory loop, the visuospatial sketchpad and the episodic buffer.

The Central Executive

In its original version, the central executive was considered an overall system capable of processing and storing. However, Baddeley and Logie (1999) proposed that it only has attention capacity.

Subsequent studies have proposed to complement the executive system with the episodic buffer as other separate storage system: "the episodic buffer clearly does represent a change within the working memory framework, whether conceived as a new component, or as a fractionation of the older version of the central executive"( Baddeley, 2000).

Baddeley and Logie understand the central executive as the result of the integration of several processes: the ability to focus attention, the ability to divide attention between two or more tasks, and the ability to control long-term memory access ( Baddeley et al., 1991; Logie et al., 2004; Baddeley, 2007). The way to accomplish this may be with one or more types of inhibition ( Engle et al., 1999; Miyake et al., 2000). This approach accepts that the frontal lobes play an important role in executive control, although there are differing opinions on the functions' precise location ( Duncan and Owen, 2000; Shallice, 2002).

Visuospatial Sketchpad

It has been suggested that the sketchpad's main function is to create and maintain a visuospatial representation that persists through the irregular form found in eye movement and that characterizes our exploration of the visual world ( Luck, 2007).

The visuospatial sketchpad seems to involve a number of areas, predominantly in the brain's right hemisphere. On the one hand, it contains a visual component that reflects the processing and storage of objects and their visual features. On the other hand, it contains a second parietal area, presumably involved in spatial aspects.

Phonological Buffer

With respect to cerebral location, the phonological loop is found in the brain's left hemisphere "The loop is assumed to hold verbal and acoustic information using a temporary store and an articulatory rehearsal system, which clinical lesion studies, and subsequently neuroradiological studies, suggested are principally associated with Brodmann areas, 40 and 44, respectively" ( Baddeley, 2000).

Episodic Buffer

The verbal and visual systems within the conventional model of working memory may explain many aspects, but Baddeley (2000) points out that evidence from patients with short-term memory deficits- who resist memorizing prose (with a verbal span much higher than that of isolated words) and resist serial memory of articulatory suppression- leads to supposing that a storehouse of additional support exists. This is seen in the existence of a new mechanism that combines information from multiple subsystems into a form of temporal representation. Baddeley (2000) proposed the term episodic buffer for this new kind of representation.

The episodic buffer is thus a temporary storage system capable of integrating information from different sources, likely controlled by the central executive. "The buffer is episodic in the sense that it holds episodes whereby information is integrated across space and potentially extended across time" ( Baddeley, 2000). It can be preserved in patients with advanced amnesia and severe impairment of long-term episodic memory.

With that said, it is possible to consider the episodic buffer as conceptual short-term memory. Studies to date do not specify activity in a specific area. As Potter (1999) said: "The conceptual short-term memory hypothesis proposes that when a stimulus is identified, its meaning is rapidly activated and maintained briefly in conceptual short-term memory."

Long-Term Memory

Long-term memory refers to unlimited storage information to be maintained for long periods, even for life. There are two types of long-term memory: declarative or explicit memory and non-declarative or implicit memory.

Explicit memory refers to information that can be consciously evoked. There are two types of declarative memory: episodic memory and semantic memory. For its part, implicit memory encompasses all unconscious memories, such as certain abilities or skills. There are four types of implicit memory, including procedural, associative, non-associative, and priming.

Declarative/Explicit Memory

Explicit memory refers to information that can be evoked consciously. There are two types of declarative memory: episodic memory and semantic memory. As shown below, episodic memory stores personal experiences and semantic memory stores information about facts.

Episodic Memory

"Episodic memory involves the ability to learn, store, and retrieve information about unique personal experiences that occur in daily life. These memories typically include information about the time and place of an event, as well as detailed information about the event itself." ( Dickerson and Eichenbaum, 2010).

There are a number of neural components that are closely related to the proper functioning of episodic memory, which include the following: the cortex near the hippocampus [as discussed below, the perirhinal cortex (PRC), the entorhinal cortex, and the parahippocampal cortex (PHC)], cortical and subcortical structures, and the circuits within the medial temporal lobe and hippocampus.

The cortices near the hippocampus extensively interact with a number of cortical and subcortical structures; cortical components have key roles in various aspects of perception and cognition, while the medial temporal lobe structures mediate the organization and the persistence of the memory network, whose data is stored in these cortical areas ( Dickerson and Eichenbaum, 2010).

The structures directly related to the hippocampus include the entorhinal, the parahippocampal, and the perirhinal cortices. Each one is discussed in detail below.

The entorhinal cortex is the main interface between the hippocampus and neocortex, thus it is associated with the distribution of information to and from the hippocampus. The surface layers (II and III) of the entorhinal cortex project out toward the dentate gyrus and hippocampus. While layer II mainly projects out toward the dentate gyrus and the CA3 region of the hippocampus, layer III mainly projects out toward the hippocampal CA1 region and the subiculum. These layers receive input signals from other cortical areas, particularly the association cortices, the PRC and the parahippocampal gyrus, as well as the prefrontal cortex. Layers II and III receive highly processed inputs from each sensory modality, and inputs related to ongoing cognitive processes. Deep layers, particularly layer V, receive one of the three output signals from the hippocampus and, in turn, exchange connections with other cortical areas that project out toward the superficial entorhinal cortex.

Finally the hippocampus is responsible for the formation and retrieval of memories. That is, the information that the three cortices described above process reach the hippocampus where new memories are generated and from which they can later be retrieved. Episodic memory recall involves a spatial and temporal context of specific experiences. For further review of the mechanisms of memory formation see Craver (2003).

As Dickerson and Eichenbaum (2010) point out in their review, "several investigators have argued that animals are indeed capable of remembering the context in which they experienced specific stimuli, and that this capacity also depends on the hippocampus." Ergorul and Eichenbaum (2004) published a significant study to this effect in which they developed a series of tasks for rats to assess their memory of events, which combined an odor (what), the place of the experience (where), and the relation to other experiences (when). The rats were presented with a sample of an odor in one specific place along the edge of a large open field. Subsequently, as a way of testing their memory, they were presented with a choice between two arbitrarily selected odors in their original locations. The results of the test showed that normal rats use a combination of where and what information to judge the timing of the events, while rats with a damaged hippocampus cannot manage to effectively combine what, when, and where information in order to form a recovered memory.

Three years later Eichenbaum et al. (2007) proposed a functional organization of memory's medial temporal lobe system: "Neocortical input regarding the object features ("what") converges in the PRC and lateral entorhinal area (LEA), whereas details about the location ("where") of objects converge in the PHC and medial entorhinal area (MEA). These streams converge in the hippocampus, which represents items in the context in which they were experienced. Reverse projections follow the same pathways back to the parahippocampal and neocortical regions"( Eichenbaum et al., 2007).

It should be noted that memory of faces is typically associated with activity in the perirhinal and hippocampus rostral regions, while memory of objects is typically associated with wider-ranging activity ( Preston et al., 2010).

Both results concerning functional an anatomic and characterizations in animal models are consistent with the hypothesis that is guided by anatomic criteria about the functional organization of the hippocampal system ( Dickerson and Eichenbaum, 2010).

"The ventral temporal cortex, including fusiform gyrus, is commonly engaged when pictures of visual objects are presented, and the lateral temporal cortex including superior temporal gyrus is typically engaged during the encoding of auditory information" ( Dickerson and Eichenbaum, 2010).

Semantic Memory

As noted, in the context of long-term memory, there were two types of memory, corresponding to declarative and non-declarative memory. Within declarative memory, we find both episodic memory, as discussed above, and semantic memory, as discussed below.

Human beings have the ability to represent concepts in language. This ability allows us not only to disseminate conceptual knowledge to others, but also to manipulate, associate, and combine these concepts. Therefore, as Binder and Desai shows, "humans use conceptual knowledge for much more than merely interacting with objects. All of human culture, including science, literature, social institutions, religion, and art, is constructed from conceptual knowledge" ( Binder and Desai, 2011). Activities such as reasoning, planning for the future or reminiscing about the past depend on the activation of concepts stored in semantic memory ( Mahon and Caramazza, 2008).

Binder and Desai showed two striking results related to neuroimaging research: on the one hand, the participation of the specific sensory, motor and emotional modality in understanding language and, on the other hand, the existence of large regions of the brain (the inferior parietal lobe and a large part of the temporal lobe) involved in tasks related to understanding. These latter regions converge on the many currents involved in perception processing, and these convergences allow supramodal representations of perceptual experience that support a variety of conceptual functions, including language, social cognition, object recognition, and the extraordinary human ability to remember the past and imagine the future ( Binder and Desai, 2011). Therefore, accepting their argument, semantic memory consists of two representations: a specific modality and supramodal modality.

In this regard, Binder and Desai found several objections. A not inconsiderable one is that activations observed in imaging experiments could be an epiphenomenon rather than causally related to understanding. Therefore, the involvement of the motor system for processing a text would contribute to understanding and is not a mere product. Another critical point is the possibility of interpreting that collected activations represent images after understanding takes place. However, and as they showed in their review ( Binder and Desai, 2011), in studies of neuroimaging with high temporal resolution, the activation of motor regions during the processing of a text appears to be rapid, about 150-200 ms after each word ( Pulvermüller et al., 2005; Boulenger et al., 2006; Hoenig et al., 2008; Revill et al., 2008).

"These converging results provide compelling evidence that sensory-motor cortices play an essential role in conceptual representation. Although it is often overlooked in reviews of embodied cognition, emotion is as much a modality of experience as sensory and motor processing ( Vigliocco et al., 2009). Words and concepts vary in the magnitude and specific type of emotional response they evoke, and these emotional responses are a large part of the meaning of many concepts" ( Binder and Desai, 2011).

As observed by Binder and Desai: "These modal convergence zones then converge with each other in higher-level cortices located in the inferior parietal lobe and much of the ventral and lateral temporal lobe (...). One function of these high-level convergences is to bind representations from two or more modalities, such as the sound and visual appearance of an animal, or the visual representation and action knowledge associated with a hand tool ( Wernicke, 1974; Damasio, 1989; Barsalou, 1999b; Patterson et al., 2007). Such supramodal representations capture similarity structures that define categories, such as the collection of attributes that place 'pear' and 'light bulb' in different categories despite a superficial similarity of appearance, and 'pear' and 'pine-apple' in the same category despite very different appearances ( Rogers and McClelland, 2004). More generally, supramodal representations allow the efficient manipulation of abstract, schematic conceptual knowledge that characterizes natural language, social cognition, and other forms of highly creative thinking ( Dove, 2011; Diefenbach et al., 2013)" ( Binder and Desai, 2011).

Non-declarative/Implied Memory

As noted, long-term memory refers to unlimited information storage that can be maintained for long periods, even for life. There are two types of long-term memory: declarative or explicit memory and non-declarative or implied memory.

Implicit memory encompasses all unconscious memories, as well as certain abilities or skills. There are four types of implicit memory: procedural, associative, non-associative, and priming. Each one is detailed below.

Procedural Memory: Habits and Skill

Procedural memory is the part of memory that participates in recalling motor and executive skills that are necessary to perform a task. It is an executive system that guides activity and usually works at an unconscious level. When necessary, procedural memories are retrieved automatically for use in the implementation of complex procedures related to motor and intellectual skills.

Development of these rote capacities occurs through procedural learning, that is, by systematically repeating a complex activity until acquiring and automatizing the capacity of all neural systems involved in performing the task to work together.

The acquisition of skills requires practice. However, the simple repetition of a task does not ensure skill acquisition. A skill is thought to be acquired when behavior changes as a result of experience or practice. This is known as learning and it is not a directly observable phenomenon. Here we will discuss two models for acquiring skills.

The first model comes from Fitts's team ( Fitts, 1954; Fitts and Posner, 1967). These scientists propose an explanatory model of skill acquisition, based on the idea of learning as a process in three phases:

(a) Cognitive phase: The process begins with the acquisition of knowledge about the factors that make up a particular observed behavior. At this point, the psychological process of attention is important. The skill to be acquired must be broken down into its basic components and one must understand how these components are combined to form a whole in the correct execution of the task ( Fitts, 1954; Fitts and Posner, 1967).

(b) Associative phase: Individual repeated practice takes place until there is an automatic response pattern. As one progresses through this point, the actions that are important for the implementation of a skill are learned and become automated, just as any superfluous or ineffective actions disappears. The individual sensory system acquires the exact symbolic and spatial data required for the appropriate execution of the skill ( Fitts, 1954; Fitts and Posner, 1967).

(c) Autonomous/procedural phase: This is the final phase and it consists in perfecting acquired skills. The ability to judge which stimuli are important and unimportant improves and a lower level of conscious thought is required because the skill becomes automated ( Fitts, 1954; Fitts and Posner, 1967).

The other model corresponds to ( Tadlock, 2005) and is called Predictive Cycle. This model proposes that learning only requires conscious maintenance of the desired end result. The model consists of the following phases: Trial, error, implicit result analysis, and decision-making at the implicit level of the way in which execution of the next test must be changed for successful implementation. These steps are repeated again and again until the subject builds or remodels his/her neural network so that it can guide the activity without the need for conscious thought.

A number of factors are involved when acquiring and implementing skills, including attention and pressure. For the acquisition of a new skill one must pay attention to the steps to be undertaken. This process involves using working memory to allow for connecting the different steps involved. Procedural memory acquires the habit with the help of the attention span, but it implies a lesser performance. However, with practice, procedural knowledge is developed. Procedural knowledge operates away from working memory, which allows for the implementation of the most automated skills ( Anderson, 1993). Meanwhile, pressure can affect the performance of a task in two ways: choking or clutchness. The choking phenomenon occurs when experienced and skilled performers fail under stress. Auto-focus theories suggest that pressure causes an increase in anxiety and self-consciousness concerning correct execution. This ends up causing increased attention directed toward processes directly involved in the execution of the skill ( Beilock and Carr, 2001). On the other hand, the attention span allows the habit to be acquired in refers to giving a top performance on a given task when pressure is highest.

Because they are especially relevant, we will briefly outline brain components involved in the acquisition of new skills and habits, including the basal ganglia, cerebellum and limbic system.

The cerebellum is involved in the execution of movements and the perfection of motor agility needed procedural skills. Damage to this area can impede one from relearning motor skills and recent studies have linked it to the process of automating unconscious skills during the learning phase ( Kreitzer, 2009).

The limbic system shares anatomical structures with a component of the neostriatum, which assumes primary responsibility for the control of procedural memory. There is a special protein membrane associated with the limbic system that runs through the nucleus basalis. Thus, activation of brain regions that work together during the operation of procedural memory can be followed through the protein membrane associated with the limbic system.

As a final note on procedural memory, whereas earlier theories proposed a passive role whereby memories were shielded from interfering stimuli during sleep ( Vertes and Eastman, 2000; Vertes, 2004), current theories suggest a more active role in which memories undergo a process of consolidation during sleep ( Ellenbogen et al., 2006). Furthermore, in human beings, this process of consolidation is thought to contribute to the development of procedural knowledge, especially when it occurs right after the initial phase of memory acquisition ( Karni et al., 1994; Gais et al., 2000; Stickgold et al., 2000a, b; Saywell and Taylor, 2008). Within the scope of motor skills related to procedural memory, there is evidence to show that there is no improvement in skills if followed by short NREM sleep (stages 2-4 sleep), such as a short nap ( Walker et al., 2002). However, REM sleep (a sleep phase with an increased frequency and intensity of the so-called dream state) followed by a period of slow wave sleep has proven to be the most effective combination for procedural memory consolidation, especially immediately following skill acquisition ( Siegel, 2001).

Associative Memory: Classical and Operant Conditioning

Associative memory refers to the storage and retrieval of information through association with other information. The acquisition of associative memory is carried out with two types of conditioning: classical conditioning and operant conditioning. Classical conditioning is associative learning between stimuli and behavior. Meanwhile, operant conditioning is a form of learning in which new behaviors develop in terms of their consequences. Associationist philosophers have also worked with the latter model ( Hartley, 1749; Mill, 1829). We will look more closely at both.

The close association between two stimuli over time causes classical conditioning: first a conditioned stimulus and then an unconditioned stimulus. While a conditioned stimulus does not automatically trigger a response, an unconditioned stimulus does just that. By repeating a conditioned stimulus over time before an unconditioned stimulus, a conditioned stimulus acquires characteristics that simulate being necessary for an unconditioned stimulus. Pavlov's Dog ( Pavlov, 1927) is a clear example. The dog produces saliva when it detects the presence of food (unconditioned stimulus). If the sound of a bell goes off (conditioned stimulus) during the act of giving the dog food, the dog will associate the sound of the bell with the presence of food. In successively repeating this, the dog will associate the unconditioned stimulus with the conditioned stimulus, thus producing saliva when just hearing the bell.

Although Skinner is considered to be the originator of operant conditioning, his research drew upon Thorndike's law of effect. For operant conditioning, as has already been mentioned, positive consequences following a behavior promote its repetition. Conversely, if the behavior involves negative consequences, the behavior will be repeated less. Thorndike (1932) called this conditioning instrumental because it suggests that the behavior serves as a means to an end and emerges from trial and error. Skinner later coined the term that is now widely associated with this law of effect - reinforcement ( Skinner, 1938).

Non-associative Memory: Habituation and Sensitization

Non-associative memory is one of three types of non-declarative or implicit memory and refers to newly learned behavior through repeated exposure to an isolated stimulus.

New behavior can be classified into two processes: sensitization and habituation ( Alonso, 2008). Before delving into each process, it is worth noting that the simplicity in acquiring this type of memory has advanced knowledge of the learning process. This is due to the fact that both animals and human beings have these two processes, such that it is very likely that, in this regard, they share a molecular biological basis. Kandel (1976) proposed a model to explain habituation and sensitization's operation mechanism. On one hand, for habituation, acetylcholine is progressively consumed, decreasing the effectiveness of the stimulus and thus the motor response. Furthermore, for sensitization, the presence of serotonin, secreted by another sensory nerve terminal, causes an excess of acetylcholine. Thus an enhanced motor response emerges. Let's look at the two processes that take place in the acquisition of new behaviors- processes that are part of non-associative memory, habituation and sensitization.

Habituation, in this context, is linked to repetition. The repetition of a stimulus leads to a decrease in its response, which is known as habituation. Repeated exposure to a stimulus serves to stop responding to potentially important, but situationally irrelevant stimuli. Habituation could be due to a process of synaptic depression as a result of repeated activation. Thus, habituation is thought to be related to a decrease in the efficiency of synaptic transmission, a decrease that may be caused by a conductivity change in the membrane of the stimulated neuron's iconic channels.

Unlike habituation, sensitization consists in an increase in response to a stimulus due to the repeated introduction thereof. Although the processes that produce sensitization are the same as those that produce habituation, sensitization's effects are the opposite since it results in an increase of the original response. The process of sensitization may be due to a provision in transmission, whether it be presynaptic or postsynaptic.

Priming

Priming, the fourth modality of non-declarative or implicit memory, is an effect whereby exposure to certain stimuli influences the response given to stimuli presented later.

An example is in order. If you present a list of words to a person that contains the word 'ball,' and then the person is asked to participate in a task to complete words, they are more likely to respond with the word ball to the presentation of the word bowl than if they had not previously seen that word in the original list. Thus, the priming capacity can affect the choice of a particular word on a test to complete words, even long after conscious recollection of the primed words has been forgotten.

Another context where this can be seen is in asking a participant to identify an image from a small fragment. The participant is shown a larger portion of the image over time, giving them the ability to identify the image at the end. The participant will take longer to identify the image if it is the first time he/she sees it. But if he/she already saw it in a previous trial, he/she takes less time ( Kolb and Whishaw, 2003).

Future Directions

In spite of recent progress, a number of important questions remain to be tackled.

Other recent work shows the critical role of dopamine as a signal that promotes the stable incorporation of novel information into long-term hippocampal memory ( Otmakhova et al., 2013). Indeed, dopamine neurons can be activated by novelty in the absence of reward and it is thought that this activation occurs via a polysynaptic pathway that runs from the hippocampus to the dopamine cells of the VTA. However, many aspects of this process remain unclear (see Otmakhova et al., 2013).

Also requiring further investigation are the molecular processes involved in the regulation of protein synthesis related to memory. It is now thought that protein synthesis is not only involved in the consolidation of new memories, but must also be used to re-consolidate memories that have been degraded or "destabilized" as a result of retrieval. In a recent article, Jarome et al. (2016) indicate that CaMKII controls the re-consolidation process through the regulation of proteasome activity. Another mechanism for the regulation of protein synthesis involves MicroRNAs (miRNAs), a class of short, non-coding RNAs. By regulating components of pathways required for learning and memory, miRNAs modulate the influence of epigenetics on cognition in the normal and diseased brain ( Saab and Mansuy, 2014).

Conclusion and Glossary

There are three main forms of memory: sensory memory, short-term memory, and long-term memory (Figure 3). Sensory memory refers to the retention of information coming from the senses. Short-term memory refers to information processed in a short period of time. Working memory performs this processing. Working memory consists of four elements that process information: the central executive (attention control), the visuospatial sketchpad (creates and maintains a visuospatial representation), the phonological buffer (stores and consolidates new words), and the episodic buffer (stores and integrates information from different sources). Long-term memory allows us to store information for long periods of time. This information may be retrieved consciously (explicit memory) or unconsciously (implicit memory). Explicit memory consists of episodic memory (time-related events) and semantic memory (concepts and meanings). Implicit memory has, in turn, procedural memory (motor and executive skills), associative memory (classical and operant conditioning), non-associative memory (sensitization and habituation), and priming (a primary stimulus influencing a secondary one).

FIGURE 3. Memory classification.

Finally, the following glossary includes commentary about the terminology that, in our opinion, is essential for an introductory overview, enabling interested students and professionals to effectively approach the latest memory-related discoveries. This commentary is not intended as an exhaustive definition, but rather collects relevant information to situate the reader within a complex panorama.

Associative memory: refers to the storage and retrieval of information resulting from an association (i.e., resulting from an association with other information). Two types of conditioning are involved in its acquisition: classical conditioning and operant conditioning. Classical conditioning is a kind of associative learning between stimuli and behavior, and operant conditioning is a form of learning in which new behaviors develop in terms of their consequences.

Conceptual short-term memory/episodic buffer: This is a temporary storage system capable of integrating information from different sources that is probably controlled by the central executive. It is episodic in that it has episodes in which information is integrated through space and, potentially, extended through time.

Echoic memory: sensory memory that receives and processes auditory information.

Episodic memory: "involves the ability to learn, store, and retrieve information about unique personal experiences that occur in daily life. These memories typically include information about the time and place of an event, as well as detailed information about the event itself" ( Dickerson and Eichenbaum, 2010).

Explicit/declarative memory: refers to conscious memories of previously stored experiences, facts and concepts that are verifiable through a verbal reporting of them ( Tulving, 1972).

Haptic memory: sensory memory that receives and processes information from the sense of touch.

Iconic memory: visual-sensory memory that receives and processes visual stimuli.

Implicit/non-declarative memory: this encompasses all unconscious memories, as well as certain abilities or skills. There are four types of implicit memory: procedural, associative, non-associative, and priming memory.

Long-term memory: "refers to the unlimited, continuing memory store that can hold information over lengthy periods of time, even for an entire lifetime. Long-term memory is mainly preconscious and unconscious. Information in long-term memory is to a great extent outside of our awareness, but can be called into working memory to be used when needed. Some of this information is easy to recall, but some is much more difficult to access" ( Brodziak et al., 2013).

Non-associative memory: refers to newly learned behavior due to repeated exposure to a single stimulus. The new behavior can be classified into two processes: sensitization and habituation.

Perceptual memory: memory acquired through the senses. It includes a lot of individual experience; it ranges from the simplest forms of sensory memory to the most abstract knowledge.

Priming: an effect whereby exposure to certain stimuli influences the response to subsequently presented stimuli.

Procedural memory: a memory area involved in remembering executive and motor skills necessary to perform a task. It is an executive system that guides activity and usually works on an unconscious level. When necessary, procedural memories are automatically retrieved for use in the implementation of integrated procedures related to motor and intellectual skills.

Semantic memory: refers to the memory of meanings, interpretations and concepts related to facts, information and general knowledge about the world. Semantic memory gives meaning to words and phrases that would otherwise be meaningless and allows for learning based on past experience ( Kolb and Whishaw, 2003).

Sensory memory: "Sensory memory is the capacity for briefly retaining the large amounts of information that people encounter daily" ( Siegler and Alibali, 2005).

Short-term memory: is the ability to keep a small amount of information available for a short period of time. "Short-term memory should be distinguished from working memory, which refers to structures and processes used for temporarily storing and manipulating information. The relationship between short-term memory and working memory is presented variously by different theories. The notion of working memory is broader and more general because it refers to structures and processes used for temporarily stored and manipulated information" ( Brodziak et al., 2013).

Working memory: "The term working memory refers to a brain system that provides temporary storage and manipulation of the information necessary for such complex cognitive tasks as language comprehension, learning and reasoning" ( Baddeley, 1992).

Visual memory: constituted by iconic memory, visual short-term and long-term memory.

Visual short-term memory/visuospatial sketchpad: sketchpad's main function is to create and maintain a visuospatial representation that persists through the irregular form found in eye movement and that characterizes our exploration of the visual world ( Luck, 2007).

Author Contributions

FG drafting of sensory and short-term memory; bibliographical review; introduction and conclusion. Revising critically and final approval of the version to be published. EC drafting of long-term memory (declarative and non-declarative); bibliographical review.

Conflict of Interest Statement

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

Acknowledgments

This work was supported by the Institute for Culture and Society (ICS) of the University of Navarra and by the "Programa de Ayudas de la Asociación de Amigos de la Universidad de Navarra." We are grateful to Mind-Brain Group of ICS and, especially, to Nathaniel Barrett Ph.D., Gonzalo Arrondo Ph.D., and Javier Bernácer Ph.D. for their suggestions.

References

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Frontiers | The Neuroanatomical, Neurophysiological and Psychological Basis of Memory: Current Models and Their Origins

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This review aims to classify and clarify, from a neuroanatomical, neurophysiological, and psychological perspective, different memory models that are current...

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Short term memory can be divided into two parts, our immediate memory and our working memory. Our immediate memory takes information from our sensory memory that was found to be important. This is where we decided how important the information is and what to do with this information. Our immediate memory usually puts more importance on information that we have an emotional tie to or are immediate threats to the person's survival. It looks at information that we have experience with first. Memories are usually in immediate memory for about 30 seconds before it is either moved to working memory or discarded.

Immediate memory is important in educational and professional environments because it allows us to communicate with others efficiently. Immediate memory allows us to process what is being said so that we can effectively respond and recall important information from the conversation. Our immediate memory can also help us with task management and problem solving. These skills are both important for students and professionals specially when given multiple task to complete.

Working memory is the second part of short term memory. Once our immediate memory determines what information is important it passes it to working memory. Here we manipulate and process the new information. It is in working memory where we can add onto a memory or modify a memory. Memories here that are classified as important than move to long-term memory. According to Sousa, "we can process items intently in working memory for up to 45 minutes before becoming fatigued" (Sousa, 2022). If a person is motivated to learn about a subject or topic, they can process information a little longer until they become physically tired. This is important in an educational environment. If students are not motivated or interested in a topic that is being taught, they are only processing information for a short amount of time. Therefore teachers need to chunk information. In working memory, they are able to recall the information and expand on it. This allows students to manipulate the memory in working memory. When information is in working memory, it is also important to practice or rehearse what they learned. This increases neural connections that are being made.

Working memory is important in educational environments because it allows students to take what they have learned and use that skill to problem solve or draw conclusions. This also helps with critical thinking and retaining information. Working memory in a professional environment can help with adaptability and increase productivity. Working memory allows us to recall information, this would allow for errors to be reduced and activities to be completed faster.

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Long term memory is where we can store, recall, and manage memories. When learning new things and gaining new experiences, we want these things to go to long term memory. Long term memory is important because information needs to be in long term memory for us to recall later. If we do not put something into long term memory, we will not be able to remember or access it later.

Long term memory is divided into two parts, declarative and non-declarative. Declarative memories allow us to recall things such as a phone number, a person's name, things that are more explicit. Non-declarative memories help us to remember things we have experiences with such as how to drive a car, ride a bike, things that are more implicit.

Long-term memory is important in educational environments because it allows us to recall information, skills, and experiences that we had before. Using these recalled memories, we can start to build the skill up over time. This helps students with critical thinking and problem solving since the foundation is already in long-term memory. If we do not have skills or memories in long term memories, students will have more difficulty with connecting new information to prior learning. Recalling these memories allows us to strengthen our neural connections. If we are not using certain connections they eventually are forgotten. Long term memories have the same effect in professional environments. It allows us to recall the skills we need to do our jobs effectively. When something changes or policies change, we can recall the information and modify it accordingly. This allows us to build our skills and be more proficient in our professions.

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Retention is our ability to move memories from long-term memory to working memory. In order for memories to be retained, we need to make sure they are meaningful and makes sense. This increases the likely hood that these memories will go into long term memory. We cannot recall information that we do not remember, therefore retention becomes important to learning. According to an article, "Engaging in retrieval practice significantly boosts long term retention" (Roediger, 2018). Strengthening our retention will help with our ability to recall information after a period of time. Accessing these memories are important for us to continue strengthening our neuron connections. If we can access information that is needed to connect new learning, this helps make the skills more meaningful. This in turn allows us to do better with critical thinking and in problem solving.

Retention is also closely related to learning. When we learn new information we want to connect it to prior knowledge that we have. In order to do this, we rely on retention and our ability to access these memories. If we are not able to access these memories or our prior experiences, we have a difficult time learning the new concept since it does not make sense and is foreign to us. This reduces our ability to move it to long-term memory.

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Memories are the internal mental records that we maintain, which give us instant access to our personal past, complete with all of the facts that we know and the skills that we have cultivated. While the mind’s capacity to store and recall…

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Transfer can be broken into transfer of learning and transfer during learning. When we are talking about transfer during learning, we are using our prior experiences and knowledge to help make connections to the new material we are learning. This helps us to build the skills we already have in our long term memory. During this type of transfer, our hippocampus is given a signal and filters through all of our long term memory to find memories that are related or connected to what we are currently learning. When we talk about transfer of learning, we are explaining how we recall information for future use.

When we are taking in more data about a skill or concept, our brain is filtering through our long term memories. Any data that is related is retrieved and brought to working memory. Here we connect our prior knowledge with our new knowledge. This is the first part of transfer. Once we connect the information, we can then use it and recall it in the future. This is the second part of transfer.

Transfer of memories can be positive or negative. When we are transferring memories that uses a skill we already know it can help us to learn a new similar skill. For instance, learning a new language that is similar to a language you already know can help you to be successful in that new language. On the other hand, some memories can make learning more difficult which would be a negative transfer. Some memories can affect how we understand a new skill. For instance, if we are taught one way of writing a paper and another teacher teaches a new way, our prior knowledge can hinder how we understand the new approach.

Transfer is important when it comes to learning, because we are constantly wanting to connect what we are learning to things we already have experience with. This makes our new learning understandable and more meaningful. According to the textbook, "The more information a student can transfer from their schooling to the context of everyday life, the greater the probability that they will be good communicators, critical thinkers and successful problem-solvers" (Sousa, 2022, pg. 126).

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Our working memory can only focus on new information for a limited amount of time before it becomes fatigued. Once our working memory gets fatigued, we are no longer able to process information and our chances of moving it to long term memory declines. For us to be able to improve our memory we can use a strategy called chunking. Chunking is when we break complex information into smaller pieces. This allows us to focus on smaller parts of the complex skill and can be done in working memory before it gets fatigued. This will increase our ability to move it to long term memory. According to an article, "research indicates that chunking not only aids in recalling chunked information but also supports the retention of other concurrently held data in working memory" (Norris, 2021). Chunking will also help when it comes to memory retrieval. When we are learning a similar skill or expanding on that skill memories are pulled from our long term memory to help us make these connections. Chunking information allows us to easily recall these memories and strengthens our memory retention.

Teachers can use chunking in the classroom to help students learn complex skills. We know that working memory only has a window of opportunity for students to retain the information. Using the chunking strategy will allow teachers to take advantage of this window. If we break these skills into chunks, we can help enhance long term retention. Students can learn part of the skill, then practice it and put it into action. The next day we can show the next part of the skill, this allows a student to recall what they learned from the last lesson. This allows them to enhance their neural connections which will help with retention.

Chunking can help with students who also deal with trauma. Students who have trauma may have decreased attention spans, working memory, and emotional regulation. Chunking will help students with trauma by shortening the amount of attention needed. Again breaking down these complex skills, can reduce the amount of time that a student with trauma needs to focus on the skill. Chunking can also help with the amount of pressure we are putting on our working memory. Students with trauma can have an impaired working memory where it gets overloaded easily. This can become stressful on a student who has trauma. Breaking this information down, can reduce the amount of stress we are putting on their working memory. This will reduce their stress level and increase their chances of moving these memories to long term memory.

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Bridging and Hugging strategy to help with transfer

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Bridging and Hugging strategy to help with transfer

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When we are moving memories from working memory to long term memory, we know the memories that make sense and have meaning are more likely to be moved to long term memory. The bridging and hugging strategy allows teachers to make things more meaningful to students. This also increases students ability to make it make sense, since we are using prior knowledge to help make the connection.

The bridging and hugging strategy helps with transfer of learning by connecting past experiences to present learning. Students then can use the new neural connection to help them with future skills and content. According to the text, "bridging and hugging are designed to help students make transfer links from past to present and from present to future" (Sousa, 2022, pg. 133). This is an important strategy in today's educational settings because it makes learning for students more relevant and engaging. The bridging strategy allows us to make connections with things that students are already aware about. This prior knowledge helps with understanding new concepts. With today's classrooms being highly diverse, this strategy supports different types of learners. When we have students who are ELL students, we can use their prior knowledge of their language to help them make connections to the content.

The bridging and hugging strategy is great for students who have experienced trauma as well. Students who experience trauma have a hard time regulating emotion and can become anxious in the classroom. By connecting prior knowledge, we can help bridge content to something they already know. This can lower anxiety in these students, making the classroom safe for students. Trauma can cause students to become disconnected from their learning since they are not able to process emotions normally. By connecting past to present, this makes learning more meaningful to these students since we are focusing on their interests and their lives.

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Trauma can cause our hippocampus, amygdala and prefrontal cortex to work differently than they normally would. When a student is experiencing stress from trauma, it causes parts of our brain to work differently. The amygdala helps us regulate emotions. It also helps us to regulate fear and if we are feeling threatened, this part of the brain helps us determine if we should flee or fight. In a student that has trauma, this part of the brain stays very active causing students to constantly stay in fight or flight mode. This reduces their ability to focus on the content and skills that we are learning since they are focusing on their safety in a given situation. The hypothalamus controls our cardiovascular system which includes our heart rate and blood pressure. Since students with trauma are constantly in this state of fear or stress, it can cause their blood pressure to increase. Our hippocampus allows for neuronal plasticity. When we have trauma, we increase the cortisol levels. With these levels being high, our hippocampus cannot create more memories, strengthen new neuronal connections, or prune the neuronal connections that are no longer needed.

Trauma can affect memory since it makes it difficult to process emotions and create memories that are emotional based. Due to the amygdala not working correctly, it causes memories to be fragmented or incomplete since it overrides the hippocampus. The hippocampus allows us to integrate details into our memories, without this we do not get the full picture or the details that are needed to create them. The prefrontal cortex is also affected by trauma, causes problems for students to pay attention for long periods of time. This in return causes problems with encoding and organizing memories.

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1. Camina, E. (2017). The Neuroanatomical, Neurophysiological and Psychological Basis of Memory: Current Models and Their Origins. Front. Pharmacol. https://doi.org/10.3389/fphar.2017.00438

2. Feng, K. (2019). Spaced Learning Enhances Episodic Memory by Increasing Neural Pattern Similarity Across Repetitions. The Society for Neuroscience.  https://doi.org/10.1523/JNEUROSCI.2741-18.2019

3. Norris, D. (2021). Chunking and data compression in verbal short-term memory. Elsevier B.V. https://doi.org/10.1016/j.cognition.2020.104534

4. Roediger, H. (2018). Remembering What We Learn. Cerebrum.

5. Sousa, D. (2022). How the Brain Works (6th Edition). SAGE publication Ltd.