Memory Accuracy Research Articles

Ayahuasca enhances the formation of hippocampal-dependent episodic memory without impacting false memory susceptibility in experienced ayahuasca users: An observational study.

Ayahuasca is an Amazonian brew with 5-HT2A-dependent psychedelic effects taken by religious groups globally. Recently, psychedelics have been shown to impair the formation of recollections (hippocampal-dependent episodic memory for specific details) and potentially distort memory while remembering. However, psychedelics spare or enhance the formation of familiarity-based memory (cortical-dependent feeling of knowing that a stimulus has been processed). Given the growing literature on the plasticity-promoting effects of psychedelics, we investigated the acute impact of ayahuasca on recollection, familiarity, and false memory in an observational study of 24 Santo Daime members with >500 lifetime ayahuasca uses on average. Participants completed a false memory task at baseline and after they consumed a self-selected dose of ayahuasca prepared by their church (average dose contained 3.36 and 170.64 mg of N,N-dimethyltryptamine and β-carbolines, respectively). Surprisingly, pre-encoding administration of ayahuasca enhanced hit rates, memory accuracy, and recollection but had no impact on familiarity or false memory. Although practice effects cannot be discounted, these memory enhancements were large and selective, as multiple measures of false memory and metamemory did not improve across testing sessions. β-carboline activity potentially accounted for this recollection enhancement that diverges from past psychedelic research. Although ayahuasca did not impact familiarity, these estimates were generally elevated across conditions compared to past work, alluding to a consequence of frequently driving cortical plasticity. When encoding and retrieval took place under acute ayahuasca effects in experienced ayahuasca users, susceptibility to memory distortions did not increase, potentially owing to enhancements in memory accuracy.

An Improved Light GBM using Bayesian Optimization for Vulnerability Exploitation Prediction

Despite the significant advances in software security research, exploitability prediction remains elusive due to the uncertainty of which vulnerability to be prioritized. Though many studies have been done on vulnerability prediction, some problems still persist such as efficient parameter optimization, which has significant effect on the algorithm performance and efficiency. To address these challenges, we proposed an Improved Light Gradient Boosting Machine (LGBM) model using Bayesian Optimization (BO) Method. Three experiments were conducted to compare prediction accuracy and computational cost of time and memory on LGBM, LGBM with Grid Search and LGBM with Bayesian Optimization models. The results demonstrated that our Improved BO- LGBM model has better prediction accuracy and lower computational cost than the comparative models. BO-LIGHT GBM rendered AUC of 83% measuring the model performance, accuracy of 81%, while in terms of time and memory consumption has definitely taken the lead of 0.23 min executional time and 32MiB system memory. Our results suggest promising future applications of our improved BO_ LGBM model for the prediction of vulnerability exploitation, that could be relevant for IT organizations and vendors or any organization that has limited computational resources in its premises if employed.

Intelligent Analysis and Prediction of Computer Network Security Logs Based on Deep Learning

Since the beginning of the 21st century, the development of computer networks has been advancing rapidly, and the world has gradually entered a new era of digital connectivity. While enjoying the convenience brought by digitization, people are also facing increasingly serious threats from network security (NS) issues. Due to the significant shortcomings in accuracy and efficiency of traditional Long Short-Term Memory (LSTM) neural networks (NN), different scholars have conducted research on computer NS situation prediction methods to address the aforementioned issues of traditional LSTM based NS situation prediction algorithms. Although these algorithms can improve the accuracy of NS situation prediction to a certain extent, there are still some limitations, such as low computational efficiency, low accuracy, and high model complexity. To address these issues, new methods and techniques have been proposed, such as using NN and machine learning techniques to improve the accuracy and efficiency of prediction models. This article referred to the Bidirectional Gated Recurrent Unit (BiGRU) improved by Gated Recurrent Unit (GRU), and introduced a multi model NS situation prediction algorithm with attention mechanism. In addition, the improved Particle Swarm Optimization (PSO) algorithm can be utilized to optimize hyperparameters and improve the training efficiency of the GRU NN. The experimental results on the UNSW-NB15 dataset show that the algorithm had an average absolute error of 0.0843 in terms of NS prediction accuracy. The RMSE was 0.0932, which was lower than traditional prediction algorithms LSTM and GRU, and significantly improved prediction accuracy.

Sleep and retrieval practice both strengthen and distort story recollection

Abstract Over time, memories lose episodic detail and become distorted, a process with serious ramifications for eyewitness identification. What are the processes contributing to such transformations over time? We investigated the roles of post learning sleep and retrieval practice in memory accuracy and distortion, using a naturalistic story recollection task. Undergraduate students listened to a recording of the “War of the Ghosts”, a Native American folktale, and were assigned to either a sleep or wake delay group, and either a retrieval practice or listen-only study condition. We found higher accuracy after sleep compared to wake in the listen-only condition, but not in the retrieval practice condition. This effect was driven by participants in the wake, retrieval practice condition showing superior memory compared to the wake, listen only condition. A similar pattern was found for memory distortion, with both sleep and retrieval practice being associated with more inferences of non-presented, but story related information, compared to the wake, listen only condition. These findings suggest both sleep and retrieval practice contribute to narrative memory stabilization and distortion. This paper is part of the Festschrift in honor of Dr. Robert Stickgold. The open access publishing fee for this paper was contributed by the Psychiatry Department at BIDMC.

Group information enhances recognition of both learned and unlearned face appearances.

Are people better at recognizing individuals of more relevant groups, such as ingroup compared to outgroup members or high-status compared to low-status individuals? Previous studies that associated faces with group information found a robust effect of group on face recognition but only tested it using the same images presented during the learning phase. They therefore cannot tell whether group information enhances encoding of the specific image presented during learning or encoding of the person who appears in it, which should generalize to other images of that person. In addition, the measures used in these studies do not sufficiently distinguish between sensitivity and response bias. In this article, we addressed these limitations and examined in three experiments the effect of group membership (Experiments 1 and 2) and social status (Experiment 3) on face recognition. In all experiments, we assessed recognition of both learned and unlearned views of the learned faces. Our results show improved recognition of ingroup members compared to outgroup members and of individuals of high-status groups compared to low-status groups for both learned and unlearned views. These effects emerged also when we used measures of memory accuracy that adequately control for response bias. These findings highlight the importance of group and status information in person recognition. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Increased memory accuracy of previous mood states in depressed patients in daily life

Depression is characterized by a loss of positive and pronounced negative memory bias, which persists after remission. While theoretical accounts of depressive realism, emotional inertia, and mood-congruency substantiate the compelling evidence of weak positive memory in depression, they cannot fully explain negative memory bias in depression. We used Ecologically Momentary Assessments (EMA) of memory bias to provide insight into the accuracy and depression status-dependency of recall of previous positive and negative mood states. Currently- (n = 46), remitted- (n = 90), and never-depressed individuals (n = 55) provided positive mood and negative mood ratings (7x/day for six days), while also recalling their recent (i.e., previous prompt; 3x/day) or distal (i.e., one day lag; 1x/day) mood states. Currently depressed individuals displayed most accuracy and hence least bias in recall of both positive and negative mood; with accuracy in currently and remitted depressed individuals being independent of their current mood state. Conversely, mood at the time of recall significantly related to memory accuracy among never-depressed individuals with more negative mood, resulting in a depressotypic memory bias. Results are consistent with depressive realism and mood-congruency accounts, as well as with evidence for loss of positive memory bias (but not for negative memory bias) in depression.

Cannabis smoke and oral Δ9THC enhance working memory in aged but not young adult subjects.

With increased legalization of recreational and medical cannabis, use of this drug is growing rapidly among older adults. As cannabis use can impair cognition in young adults, it is critically important to understand how consumption interacts with the cognitive profile of aged individuals, who are already at increased risk of decline. The current study was designed to determine how cannabis influences multiple forms of cognition in young adult and aged rats of both sexes when delivered via two translationally-relevant routes of administration. Acute exposure to cannabis smoke enhanced prefrontal cortex-dependent working memory accuracy in aged males, but impaired accuracy in aged females, while having no effects in young adults of either sex. In contrast, the same cannabis smoke exposure regimen had minimal effects on a hippocampus-dependent trial-unique non-matching to location mnemonic task, irrespective of age or sex. In a second set of experiments, chronic oral consumption of Δ9-tetrahydrocannabinol (Δ9THC) enhanced working memory in aged rats of both sexes, while having no effects in young adults. In contrast, the same oral Δ9THC regimen did not affect spatial learning and memory in either age group. Minimal age differences were observed in Δ9THC pharmacokinetics with either route of administration. Together, these results show that cannabis and Δ9THC can attenuate working memory impairments that emerge in aging. While these enhancing effects do not extend to hippocampus-dependent cognition, cannabis does not appear to exacerbate age-associated impairments in this cognitive domain.

Limited carry-over effects of socioemotional manipulations on subsequent unrelated memory tasks.

Although age is typically associated with significant impairments in memory performance, several domains exist in which these impairments are reduced or even eliminated. These "pockets of preservation" in older adults' memory can be seen in tasks involving socioemotional processing and may be supported by distinct encoding or retrieval modes relative to neutral content. The current study examines whether engaging in socioemotional tasks prior to encoding or retrieval allows older adults to enter an encoding or retrieval mode that better supports memory performance. In two online studies, adults across the lifespan were asked to complete a memory task where they incidentally encoded and retrieved neutral (Experiment 1, N = 1621) or emotional (Experiment 2, N = 409) word-image pairs. Participants were randomly assigned to a control (i.e., no manipulation), pre-encoding, or pre-retrieval socioemotional manipulation condition. There were no main effects of manipulation condition, suggesting that such manipulations may not reliably enhance memory. However, future research is needed to follow up on exploratory analyses that highlighted particular conditions under which these manipulations may convey benefits. There were also no age-by-manipulation interactions. While these null effects may suggest that these manipulations are not better suited to older adults, this may also be a result of the unexpected age-related increases (Experiment 1) and age invariance (Experiment 2) in overall memory accuracy in the current study. Socioemotional manipulations should also be examined in older adults who underperform younger adults.

Productivity forecasting of the solar still with phase change material using LSTM and GRU neural networks

Abstract Solar desalination is one of the renewable energy techniques by which freshwater can be obtained economically. Solar desalination experiments are time and resource-consuming methods; hence there is a need for a robust system to identify the serviceability of the solar still in a specific region. The objective of the present study is to develop a forecasting model using artificial neural networks to predict freshwater productivity. Specifically, the study aims to compare the accuracy of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks in forecasting the productivity of Conventional Solar Stills (CSS) and Solar Stills with Phase Change Material (SSPCM). Additionally, the research seeks to analyze the impact of advanced materials, such as phase change materials (PCM) and pin fins, on solar still productivity; determine which neural network model provides more accurate and reliable forecasts for different solar still designs; and contribute to the optimization of solar still implementations by developing predictive models applicable to diverse geographical regions. The current investigation involved analysing the experimental outcomes of a solar still that employed phase change material (PCM) and pin fins. Palmitic acid was implemented as the energy storage material and was placed beneath the absorber plate. The neural network model was trained and validated using time-series solar still experimental data. Different statistical measures were utilised to evaluate the accuracy of Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). The results indicate that the freshwater productivity forecasted by LSTM exhibited greater accuracy than GRU. Specifically, the coefficient of determination values for LSTM were 0.96 and 0.98 for the CSS and SSPCM, respectively, which were higher than the corresponding values for GRU.

Differential effects of negative valence and memory type on accuracy, confidence, and metacognitive efficiency

Emotion enhances the subjective experience of recollection. Previous research examined associations between memory accuracy and metamemory confidence judgments, but these studies have not quantified the relationship between accuracy and metacognitive confidence judgments. In this study we utilize signal detection theory frameworks to investigate how memory accuracy (measured by discrimination sensitivity) and the alignment between metamemory confidence judgments and memory accuracy (ie. metacognitive efficiency) varies for neutral and negative valence, as well as item and associative detail memory types. Our results indicate that valence and memory type have different effects on accuracy, confidence, and metacognitive efficiency. Negative valence was associated with enhanced accuracy for both items and associated details, but its relationship with response bias varied across memory types, with conservative recognition responses observed for items and liberal responses for associative details. We also observed a double dissociation between metamemory confidence judgements across valence and memory type with negative valence associated with increased confidence for item memory, but decreased confidence for associated details. Examining the association between memory accuracy and metamemory confidence revealed that metacognitive efficiency was greater for negatively valenced items compared to neutral, but this effect did not generalize to details associated with negatively valenced items. These findings advance our understanding of how arousing, negatively valenced information modulates memory and metacognition.

The Effects of the Complexity of 3D Virtual Objects on Visual Working Memory Capacity in AR Interface for Mobile Phones

The current study aims to investigate the effects of 3D virtual object complexity on visual working memory capacity in mobile augmented reality (MAR) interfaces. With the popularity of augmented reality technology in mobile applications, 3D virtual elements play a key role in interaction. However, prior studies ignored the correlation between virtual object presentation and visual working memory (VWM). Given that visual working memory capacity is closely related to overall cognitive ability, the current study explored the relationship between the complexity of 3D virtual objects and VWM capacity in AR interfaces. Sixty volunteers participated in a 5-point Likert scale rating to eliminate the interference factors of familiarity, concreteness and similarity in 3D virtual material objects. Then, we further conducted an MAR change detection paradigm experiment and successfully manipulated the complexity of 3D virtual objects and the set size. Thirty-one subjects completed the formal experiment. Three measurements (reaction time, proportion correct, and Cowan’s K) were analyzed for nine experimental conditions (three object complexity levels and three object set sizes). The results confirmed that the visual working memory capacity in mobile AR interfaces is modulated by the complexity of the 3D virtual objects and set size, which decreases with the increase in complexity and set size of the displayed 3D virtual objects. As the complexity of the 3D virtual objects increases, the amount of resources allocated to each item decreases, resulting in a decrease in memory capacity and memory accuracy. This study highlights the effectiveness of VWM capacity in MAR interface design research and provides valuable insights into determining the best way to present 3D virtual objects.

Effects of a Functional Cone Mushroom (Termitomyces fuliginosus) Protein Snack Bar on Cognitive Function in Middle Age: A Randomized Double-Blind Placebo-Controlled Trial.

Background: Due to the rising prevalence of cognitive impairment in the middle-aged and elderly population, combined with consumer demand for functional foods to improve health and well-being. Objective: This study aimed to formulate a functional cone mushroom (Termitomyces fuliginosus) (FCM) protein snack bar and evaluate its amino acid profile, phytochemical contents, biological activity and impact on cognitive function. Methods: A total of 26 middle-aged male and female participants were randomized and divided into placebo, FCM1 and FCM2 groups. Continuous consumption was performed for 6 weeks. Demographic data, body composition, cognitive function and memory were evaluated at baseline and at the end of the study period (6 weeks). Results: The event-related potential (ERP) analysis results showed a significant increase in N100 and P300 amplitude at the Fz location in participants who consumed the functional cone mushroom protein snack bar at a dose of 1 g compared to the placebo group (p = 0.015). Additionally, subjects who consumed the functional cone mushroom protein snack bar at a dose of 2 g showed a significantly increased P300 amplitude and percent accuracy of numeric working memory (p = 0.048) compared to those in the placebo group (p = 0.044). The possible underlying mechanism may involve AChE and MAO suppression activity alongside antioxidant activity. Conclusions: These data suggest that FCM can improve cognitive function and memory and may be considered for use in natural supplementation products with possible health benefits.

Impact of modality and mode of questioning and testing on memory reports

IntroductionIndividuals' memories are assessed in multiple contexts; however, depending on the context, how an individual is questioned may impact the quantity and quality of the details reported. One goal of this study is to investigate how the modality of questioning (individuals talk or write about an event) impacts memory reports. Additionally, being tested on previously learned information improves memory for that information compared to re-studying it. Consequently, another goal is to examine how questioning impacts memory reports compared to a second exposure. We utilized open-ended and pointed questions (true and false).MethodParticipants watched a short video and were questioned (Experiment 1: In-Person; Experiment 2: Virtual) about its contents immediately, 1 week, and 1 month later.ResultsThe current study found that writing leads to better quality memory reports than speaking, and the benefit is present 1 week later. Additionally, we found that writing mitigates an anticipated testing benefit, although this depended on whether a pointed or open-ended question was asked. Restudying (vs. immediate testing) led to better performance for the false pointed questions. However, the better performance operated differently depending on whether participants wrote or spoke following restudying, perhaps due to a differential criterion shift between the Restudy-Written and the Restudy-Spoken conditions.DiscussionWe conclude that the impact of the modality of questioning is influenced in several ways by the types of questions asked, which bears significance for many domains because one modality (or a combination) may be more suitable for producing more accurate memory reports as a function of different domains.

Temporal recall in the shadow of emotion: separate emotional contexts during encoding enhance the temporal source memory retrieval.

Episodic memory, with its emphasis on temporal-spatial contexts, has been a longstanding focus in memory research. While previous studies have investigated the role of emotion in temporal source memory using emotionally charged stimuli, such as emotional words or images, the influence of a separated emotional context remains less explored. This study sought to understand the impact of separate emotional contexts on temporal source memory. Participants were shown Chinese characters alongside separate emotional contexts (i.e. a neutral or negative picture) and then engaged in either a retrieval practice or a control condition. Finally, they were tested for recognition, temporal source memory, and emotional source memory for all the learned characters. Results revealed that a negative emotional context, unlike a neutral setting, enhanced the accuracy of temporal memory for adjacent neutral characters. However, this negative context reduced the accuracy of recalling the associated emotion. Importantly, the boost in temporal memory due to the emotional context remained even when participants were unsure or mistaken about the associated emotion. This study demonstrates the complex interplay between emotion and temporal memory, underscoring the enhancement effect of separated emotional contexts on temporal recall, irrespective of explicit emotional memory retrieval.

Cognitive Health Benefits of Everyday Physical Activity in a Diverse Sample of Middle-Aged Adults.

Physical activity has been associated with a wide range of health benefits including long-term benefits for cognitive and brain health. Whether episodes of everyday physical activity are associated with immediate cognitive benefits remains unknown. The purpose of the current study was to examine whether episodes of physical activity, occurring over the course of participants' daily lives, are associated with short-term improvements in cognitive health. Participants completed a 9-day ecological momentary assessment protocol involving 5 daily assessments of self-reported physical activity and ambulatory cognitive assessments of processing speed and visuospatial working memory. Data were analyzed in a multilevel modeling framework to explore changes in performance on each task associated with physical activity during the period leading up to the assessment as well as individual differences in average frequency of physical activity. Results of MLMs indicated that engaging in physical activity during the period (~3.5hr) leading up to an assessment was associated with improvements in processing speed equivalent to 4 years of cognitive aging. Such improvements were observed for both light and moderate-to-vigorous physical activity levels. No association was observed for visuospatial working memory accuracy; however, response time during the working memory task reliably mirrored the association observed for processing speed. The short-term benefits were observed, particularly, for individuals with an overall higher frequency of reported physical activity. Our findings suggest that engaging in everyday physical activity of any intensity level may have short-term, acute benefits for cognitive health and point to new potential targets for intervention. NCT03240406.

Win Your Race Goal: A Generalized Approach to Prediction of Running Performance.

We introduce a novel approach for predicting running performance, designed to apply across a wide range of race distances (from marathons to ultras), elevation gains, and runner types (front-pack to back of the pack). To achieve this, the entire running logs of 15 runners, encompassing a total of 15,686 runs, were analyzed using two approaches: (1) regression and (2) time series regression (TSR). First, the prediction accuracy of a long short-term memory (LSTM) network was compared using both approaches. The regression approach demonstrated superior performance, achieving an accuracy of 89.13% in contrast, the TSR approach reached an accuracy of 85.21%. Both methods were evaluated using a test dataset that included the last 15 runs from each running log. Secondly, the performance of the LSTM model was compared against two benchmark models: Riegel formula and UltraSignup formula for a total of 60 races. The Riegel formula achieves an accuracy of 80%, UltraSignup 87.5%, and the LSTM model exhibits 90.4% accuracy. This work holds potential for integration into popular running apps and wearables, offering runners data-driven insights during their race preparations.

Implicit memory reduced selectively for negative words with aging.

Disproportionally better memory for positive versus negative information (mnemonic positivity effect, MPE) in older versus younger adults has been reported on tests of explicit memory (direct, intentional) as measured by recall and recognition. The purpose of this investigation was to examine whether the MPE would be observed for implicit memory (indirect, unintentional) under conditions where, based on previous research using single words, it was expected that the MPE for explicit memory would be absent. This study investigated the influence of age on explicit and implicit memory for positive, negative, and neutral single words as measured by yes/no recognition and word identification on 24 older adults (aged 66-85) and 24 younger adults (aged 18-37) recruited from community centers in South Boston, Massachusetts. Older adults had lower recognition memory accuracy for positive, negative, and neutral words than younger adults, and, consistent with most prior studies, did not exhibit an explicit memory MPE for single words. For both groups, recognition accuracy was greatest for negative words, and was similar for positive and neutral words. In contrast, older adults exhibited implicit repetition priming, as measured by superior identification performance for repeated words, that was similar to younger adults for positive and neutral words. In younger adults, implicit memory was significantly greater for negative words than for positive and neutral words, whereas in older adults there were no significant differences in implicit memory for negative, positive, and neutral words. Therefore, selectively reduced priming for negative words in older adults was found in the context of enhanced priming for negative words in the younger adults. These findings show that there was an implicit memory MPE in older adults for words even under conditions where there was no explicit memory MPE in the older adults. Dampening of negative valence implicit memory with aging expands the perimeter of the age-related positivity framework.

Holiday or hell? Emotion regulation and memory of depressive symptoms during lockdown.

The ongoing repercussions of the COVID-19 pandemic provide an unparalleled context to examine how distressing events are remembered. Prior theory and research suggest that (a) distress during lockdowns may fade and be remembered as less distressing, or remain salient and be remembered as more distressing, than initially experienced and (b) emotional suppression and cognitive reappraisal may predict these memory biases. We test these possibilities by assessing depressive symptoms and emotion regulation during two lockdowns: at the start of the pandemic (Lockdown 2020) and 17 months later (Lockdown 2021) in a sample of parents with young children (N = 272). We assessed tracking accuracy, directional bias, and projection bias in memory of depressive symptoms in Lockdown 2020, and the moderating role of emotion regulation at the time of encoding (Lockdown 2020) and recall (Lockdown 2021). People experiencing more depressive symptoms later in the pandemic (2021) remembered the start of the pandemic (2020) to involve more depressive symptoms than initially experienced (projection bias). People engaging in greater emotional suppression at recall (2021) were less likely to project their current depressive symptoms onto memory of the prior lockdown (lower projection bias) and remembered feeling lower depressive symptoms than initially experienced (underestimation directional bias). By contrast, people engaging in cognitive reappraisal at recall (2021) remembered feeling greater depressive symptoms than they initially experienced (overestimation directional bias). These unexpected results indicate that emotion regulation may shape memory of emotion during real-life challenges differently than patterns observed in lab-based research and may reflect helpful coping with life events. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Improved RNN Model for Real-Time Human Activity Recognition

This study focuses on the automatic detection of human actions in video streams. The requirement to detect what human activities happen in videos is recognition of human action due to significant differences in people's visual and motion appearance and actions, camera perspective shifts, moving background, occlusions, noise, and a massive amount of video data. The human activity recognition challenge involves identifying physical activities carried out by individuals or groups based on traces of movements, including gestures, actions, interactions, and group activities. The detection of concepts usually requires additional annotations for the training dataset. In this paper, useful methods for categorizing human action recognition are discussed. The current models are an accurate deep learning method that is based on models that have been changed to be more useful. The large disparities that result from the backdrop and the size of the objects have prevented the identification of activities in videos from being fully and effectively addressed. The main objective is to achieve better accuracy for the Long Short-Term Memory (LSTM) method, which was used to improve the Recurrent Neural Networks (RNN) model. In this paper, LSTM is used to come up with models for different action recognition tasks. The model was made better by making the LSTM have four layers and putting 128 units, 64 units, 32 units, and 16 units in each layer, respectively. In addition, the performance evaluation of deep learning-based approaches has been compared to other related works. Therefore, an improved approach to RNN is proposed to recognize human actions. To classify the videos, a multilayer RNN with a specific type of LSTM is used to extract features from video sequences. The UCF-101 and UCF Sports human action recognition datasets are utilized in this study for both training and assessment. Test findings demonstrate that the suggested strategy achieved increased accuracy. Finally, the enhanced RNN model's total model accuracy in the UCF-101 dataset is 93.78% and 95.70% for the UCF Sport dataset.

Music cue during slow wave sleep improves visuospatial memory consolidation.

The active system consolidation theory assumes that sleep between encoding and retrieval promotes memory consolidation. In the present study, we cued new memories during slow-wave (SWS) or rapid eye movements (REM) sleep stages by presenting an instrumental music stimuli that had been previously presented during a learning session. In a within-subjects design, 18 participants slept for three nonconsecutive nights (cue during SWS, cue during REM, and no cue during control night) and were trained in a visuo-spatial memory task. The administration of cue during SWS produced better memory accuracy in comparison with REM and the control condition.