Item-specific neural representations during human sleep support long-term memory.

Understanding how individual memories are reactivated during sleep is essential in theorizing memory consolidation. Here, we employed the targeted memory reactivation (TMR) paradigm to unobtrusively replaying auditory memory cues during human participants' slow-wave sleep (SWS). Using representational similarity analysis (RSA) on cue-elicited electroencephalogram (EEG), we found temporally segregated and functionally distinct item-specific neural representations: the early post-cue EEG activity (within 0 to 2,000 ms) contained comparable item-specific representations for memory cues and control cues, signifying effective processing of auditory cues. Critically, the later EEG activity (2,500 to 2,960 ms) showed greater item-specific representations for post-sleep remembered items than for forgotten and control cues, indicating memory reprocessing. Moreover, these later item-specific neural representations were supported by concurrently increased spindles, particularly for items that had not been tested prior to sleep. These findings elucidated how external memory cues triggered item-specific neural representations during SWS and how such representations were linked to successful long-term memory. These results will benefit future research aiming to perturb specific memory episodes during sleep.

Long-term, multi-event surprise correlates with enhanced autobiographical memory.

Neurobiological and psychological models of learning emphasize the importance of prediction errors (surprises) for memory formation. This relationship has been shown for individual momentary surprising events; however, it is less clear whether surprise that unfolds across multiple events and timescales is also linked with better memory of those events. We asked basketball fans about their most positive and negative autobiographical memories of individual plays, games and seasons, allowing surprise measurements spanning seconds, hours and months. We used advanced analytics on National Basketball Association play-by-play data and betting odds spanning 17 seasons, more than 22,000 games and more than 5.6 million plays to compute and align the estimated surprise value of each memory. We found that surprising events were associated with better recall of positive memories on the scale of seconds and months and negative memories across all three timescales. Game and season memories could not be explained by surprise at shorter timescales, suggesting that long-term, multi-event surprise correlates with memory. These results expand notions of surprise in models of learning and reinforce its relevance in real-world domains.

Reap while you sleep: Consolidation of memories differs by how they were sown.

Newly formed memories are spontaneously reactivated during sleep, leading to their strengthening. This reactivation process can be manipulated by reinstating learning-related stimuli during sleep, a technique termed targeted memory reactivation. Numerous studies have found that delivering cues during sleep improves memory for simple associations, in which one cue reactivates one tested memory. However, real-life memories often live in rich, complex networks of associations. In this review, we will examine recent forays into investigating how targeted sleep reactivation affects memories within complex paradigms, in which one cue can reactivate multiple tested memories. A common theme across studies is that reactivation consequences do not merely depend on whether memories reside in complex arrangements, but on how memories interact with one another during acquisition. We therefore emphasize how intricate study design details that alter the nature of learning and/or participant intentions impact the outcomes of sleep reactivation. In some cases, complex networks of memories interact harmoniously to bring about mutual memory benefits; in other cases, memories interact antagonistically and produce selective impairments in retrieval. Ultimately, although this burgeoning area of research has yet to be systematically explored, results suggest that the fate of reactivated stimuli within complex arrangements depends on how they were learned.

Semantic associates create retroactive interference on an independent spatial memory task.

Semantic similarity between stimuli can lead to false memories and can also potentially cause retroactive interference (RI) for veridical memories. Here, participants first learned spatial locations for "critical" words that reliably produce false memories in the Deese-Roediger-McDermott paradigm. Next, participants centrally viewed words that were semantically associated with half of the critical words. Finally, participants retrieved the spatial locations for the critical words. We found that spatial memory was worse for critical words whose semantic associates were shown versus not shown, suggesting that semantic relatedness caused RI. This effect occurred in three experiments when the interfering information was presented shortly before the spatial test but not when there was a 1-hour delay before the test, nor when the order of the spatial learning and associate learning phases were reversed. These findings suggest that RI can occur solely via semantic associates when all relevant responses and no distracting responses were available at retrieval. We consider these findings to be an example of cue overload, whereby cues can be overloaded indirectly via semantic associates, and to support the importance of both semantic similarity and temporal context in RI. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Targeted memory reactivation during sleep influences social bias as a function of slow-oscillation phase and delta power.

To understand how memories are reactivated and consolidated during sleep, experimenters have employed the unobtrusive re-presentation of memory cues from a variety of pre-sleep learning tasks. Using this procedure, known as targeted memory reactivation (TMR), we previously found that reactivation of counter-social-bias training during post-training sleep could selectively enhance training effects in reducing unintentional social biases. Here, we describe re-analyses of electroencephalographic (EEG) data from this previous study to characterize neurophysiological correlates of TMR-induced bias reduction. We found that TMR benefits in bias reduction were associated with (a) the timing of memory-related cue presentation relative to the 0.1-1.5 Hz slow-oscillation phase and (b) cue-elicited EEG power within the 1-4 Hz delta range. Although cue delivery was at a fixed rate in this study and not contingent on the slow-oscillation phase, cues were found to be clustered in slow-oscillation upstates for those participants with stronger TMR benefits. Similarly, higher cue-elicited delta power 250-1000 ms after cue onset was also linked with larger TMR benefits. These electrophysiological results substantiate the claim that memory reactivation altered social bias in the original study, while also informing neural explanations of these benefits. Future research should consider these sleep physiology parameters in relation to TMR applications and to memory reactivation in general.

Real-time, closed-loop, or open-loop stimulation? Navigating a terminological jungle.

Recent advancements in real-time brain stimulation in the sleep field have led to many exciting findings. However, they have also opened up terminological ambiguities about what constitutes "open-loop", "closed-loop", and "real-time" designs. Here, we address core theoretical aspects of these terms in the hopes of strengthening future research on this topic.

Real-time stimulation during sleep: prior findings, novel developments, and future perspectives.

Real-time brain stimulation is a powerful technique that continues to gain importance in the field of sleep and cognition. In this special issue, we collected 14 articles about real-time stimulation during sleep, including one review, 12 research articles and one letter covering both human and rodent research from various fields. We hope this special issue sparks greater interest and inspires fellow sleep researchers and clinicians to develop new ideas in the exciting topic of real-time stimulation.

Semantic relatedness retroactively boosts memory and promotes memory interdependence across episodes.

Two fundamental issues in memory research concern when later experiences strengthen or weaken initial memories and when the two memories become linked or remain independent. A promising candidate for explaining these issues is semantic relatedness. Here, across five paired-associate learning experiments (N=1000), we systematically varied the semantic relatedness between initial and later cues, initial and later targets, or both. We found that learning retroactively benefited long-term memory performance for semantically related words (vs. unshown control words), and these benefits increased as a function of relatedness. Critically, memory dependence between initial and later pairs also increased with relatedness, suggesting that pre-existing semantic relationships promote interdependence for memories formed across episodes. We also found that modest retroactive benefits, but not interdependencies, emerged when subjects learned via studying rather than practice testing. These findings demonstrate that semantic relatedness during new learning retroactively strengthens old associations while scaffolding new ones into well-fortified memory traces.

Spatial gist extraction during human memory consolidation.

Theories of memory consolidation suggest that initially rich, vivid memories become more gist-like over time. However, it is unclear whether gist-like representations reflect a loss of detail through degradation or the blending of experiences into statistical averages, and whether the strength of these representations increases, decreases, or remains stable over time. We report three behavioral experiments that address these questions by examining distributional learning during spatial navigation. In Experiment 1, human subjects navigated a virtual maze to find hidden objects with locations varying according to spatial distributions. After 15 minutes, 1 day, 7 days, or 28 days, we tested their navigation performance and explicit memory. In Experiment 2, we created spatial distributions with no object at their mean locations, thereby disentangling learned object exemplars from statistical averages. In Experiment 3, we created only a single, bimodal distribution to avoid possible confusion between distributions and administered tests after 15 minutes or 28 days. Across all experiments, and for both navigation and explicit tests, representations of the spatial distributions were present soon after exposure, but then receded over time. These findings suggest gist-like representations do not improve over time, helping to clarify the temporal dynamics of consolidation in human learning and memory. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Multiple memories can be simultaneously reactivated during sleep as effectively as a single memory.

Memory consolidation involves the reactivation of memory traces during sleep. If different memories are reactivated each night, how much do they interfere with one another? We examined whether reactivating multiple memories incurs a cost to sleep-related benefits by contrasting reactivation of multiple memories versus single memories during sleep. First, participants learned the on-screen location of different objects. Each object was part of a semantically coherent group comprised of either one, two, or six items (e.g., six different cats). During sleep, sounds were unobtrusively presented to reactivate memories for half of the groups (e.g., "meow"). Memory benefits for cued versus non-cued items were independent of the number of items in the group, suggesting that reactivation occurs in a simultaneous and promiscuous manner. Intriguingly, sleep spindles and delta-theta power modulations were sensitive to group size, reflecting the extent of previous learning. Our results demonstrate that multiple memories may be consolidated in parallel without compromising each memory's sleep-related benefit. These findings highlight alternative models for parallel consolidation that should be considered in future studies.

BrainIAK: The Brain Imaging Analysis Kit.

Functional magnetic resonance imaging (fMRI) offers a rich source of data for studying the neural basis of cognition. Here, we describe the Brain Imaging Analysis Kit (BrainIAK), an open-source, free Python package that provides computationally optimized solutions to key problems in advanced fMRI analysis. A variety of techniques are presently included in BrainIAK: intersubject correlation (ISC) and intersubject functional connectivity (ISFC), functional alignment via the shared response model (SRM), full correlation matrix analysis (FCMA), a Bayesian version of representational similarity analysis (BRSA), event segmentation using hidden Markov models, topographic factor analysis (TFA), inverted encoding models (IEMs), an fMRI data simulator that uses noise characteristics from real data (fmrisim), and some emerging methods. These techniques have been optimized to leverage the efficiencies of high-performance compute (HPC) clusters, and the same code can be se amlessly transferred from a laptop to a cluster. For each of the aforementioned techniques, we describe the data analysis problem that the technique is meant to solve and how it solves that problem; we also include an example Jupyter notebook for each technique and an annotated bibliography of papers that have used and/or described that technique. In addition to the sections describing various analysis techniques in BrainIAK, we have included sections describing the future applications of BrainIAK to real-time fMRI, tutorials that we have developed and shared online to facilitate learning the techniques in BrainIAK, computational innovations in BrainIAK, and how to contribute to BrainIAK. We hope that this manuscript helps readers to understand how BrainIAK might be useful in their research.

Behavioral, Physiological, and Neural Signatures of Surprise during Naturalistic Sports Viewing.

Surprise signals a discrepancy between past and current beliefs. It is theorized to be linked to affective experiences, the creation of particularly resilient memories, and segmentation of the flow of experience into discrete perceived events. However, the ability to precisely measure naturalistic surprise has remained elusive. We used advanced basketball analytics to derive a quantitative measure of surprise and characterized its behavioral, physiological, and neural correlates in human subjects observing basketball games. We found that surprise was associated with segmentation of ongoing experiences, as reflected by subjectively perceived event boundaries and shifts in neocortical patterns underlying belief states. Interestingly, these effects differed by whether surprising moments contradicted or bolstered current predominant beliefs. Surprise also positively correlated with pupil dilation, activation in subcortical regions associated with dopamine, game enjoyment, and long-term memory. These investigations support key predictions from event segmentation theory and extend theoretical conceptualizations of surprise to real-world contexts.

Finding the Pattern: On-Line Extraction of Spatial Structure During Virtual Navigation.

While navigating the world, we pick up on patterns of where things tend to appear. According to theories of memory and studies of animal behavior, knowledge of these patterns emerges gradually over days or weeks via consolidation of individual navigation episodes. Here, we discovered that navigation patterns can also be extracted on-line, prior to the opportunity for off-line consolidation, as a result of rapid statistical learning. Thirty human participants navigated a virtual water maze in which platform locations were drawn from a spatial distribution. Within a single session, participants increasingly navigated through the mean of the distribution. This behavior was better simulated by random walks from a model that had only an explicit representation of the current mean, compared with a model that had only memory for the individual platform locations. These results suggest that participants rapidly summarized the underlying spatial distribution and used this statistical knowledge to guide future navigation.

Targeted Memory Reactivation during Sleep Elicits Neural Signals Related to Learning Content.

Retrieval of learning-related neural activity patterns is thought to drive memory stabilization. However, finding reliable, noninvasive, content-specific indicators of memory retrieval remains a central challenge. Here, we attempted to decode the content of retrieved memories in the EEG during sleep. During encoding, male and female human subjects learned to associate spatial locations of visual objects with left- or right-hand movements, and each object was accompanied by an inherently related sound. During subsequent slow-wave sleep within an afternoon nap, we presented half of the sound cues that were associated (during wake) with left- and right-hand movements before bringing subjects back for a final postnap test. We trained a classifier on sleep EEG data (focusing on lateralized EEG features that discriminated left- vs right-sided trials during wake) to predict learning content when we cued the memories during sleep. Discrimination performance was significantly above chance and predicted subsequent memory, supporting the idea that retrieval leads to memory stabilization. Moreover, these lateralized signals increased with postcue sleep spindle power, demonstrating that retrieval has a strong relationship with spindles. These results show that lateralized activity related to individual memories can be decoded from sleep EEG, providing an effective indicator of offline retrieval.SIGNIFICANCE STATEMENT Memories are thought to be retrieved during sleep, leading to their long-term stabilization. However, there has been relatively little work in humans linking neural measures of retrieval of individual memories during sleep to subsequent memory performance. This work leverages the prominent electrophysiological signal triggered by lateralized movements to robustly demonstrate the retrieval of specific cued memories during sleep. Moreover, these signals predict subsequent memory and are correlated with sleep spindles, neural oscillations that have previously been implicated in memory stabilization. Together, these findings link memory retrieval to stabilization and provide a powerful tool for investigating memory in a wide range of learning contexts and human populations.

Sleep Spindles and Memory Reprocessing.

We propose a framework for the memory function of spindle oscillations during sleep. In this framework, memories are reinstated by spindle events and further reprocessed during subsequent spindle refractory periods. We posit that spindle refractoriness is crucial for protecting memory reprocessing from interference. We further argue that temporally-coordinated spindle refractory periods across local networks facilitate the consolidation of rich, multimodal representations, and that localized spindle refractoriness optimizes oscillatory interactions that support systems consolidation in the sleeping brain.

Competitive learning modulates memory consolidation during sleep.

Competition between memories can cause weakening of those memories. Here we investigated memory competition during sleep in human participants by presenting auditory cues that had been linked to two distinct picture-location pairs during wake. We manipulated competition during learning by requiring participants to rehearse picture-location pairs associated with the same sound either competitively (choosing to rehearse one over the other, leading to greater competition) or separately; we hypothesized that greater competition during learning would lead to greater competition when memories were cued during sleep. With separate-pair learning, we found that cueing benefited spatial retention. With competitive-pair learning, no benefit of cueing was observed on retention, but cueing impaired retention of well-learned pairs (where we expected strong competition). During sleep, post-cue beta power (16-30 Hz) indexed competition and predicted forgetting, whereas sigma power (11-16 Hz) predicted subsequent retention. Taken together, these findings show that competition between memories during learning can modulate how they are consolidated during sleep.

Sleep Spindle Refractoriness Segregates Periods of Memory Reactivation.

The stability of long-term memories is enhanced by reactivation during sleep. Correlative evidence has linked memory reactivation with thalamocortical sleep spindles, although their functional role is not fully understood. Our initial study replicated this correlation and also demonstrated a novel rhythmicity to spindles, such that a spindle is more likely to occur approximately 3-6 s following a prior spindle. We leveraged this rhythmicity to test the role of spindles in memory by using real-time spindle tracking to present cues within versus just after the presumptive refractory period; as predicted, cues presented just after the refractory period led to better memory. Our findings demonstrate a precise temporal link between sleep spindles and memory reactivation. Moreover, they reveal a previously undescribed neural mechanism whereby spindles may segment sleep into two distinct substates: prime opportunities for reactivation and gaps that segregate reactivation events.

Retrieval and sleep both counteract the forgetting of spatial information.

Repeatedly studying information is a good way to strengthen memory storage. Nevertheless, testing recall often produces superior long-term retention. Demonstrations of this testing effect, typically with verbal stimuli, have shown that repeated retrieval through testing reduces forgetting. Sleep also benefits memory storage, perhaps through repeated retrieval as well. That is, memories may generally be subject to forgetting that can be counteracted when memories become reactivated, and there are several types of reactivation: (i) via intentional restudying, (ii) via testing, (iii) without provocation during wake, or (iv) during sleep. We thus measured forgetting for spatial material subjected to repeated study or repeated testing followed by retention intervals with sleep versus wake. Four groups of subjects learned a set of visual object-location associations and either restudied the associations or recalled locations given the objects as cues. We found the advantage for restudied over retested information was greater in the PM than AM group. Additional groups tested at 5-min and 1-wk retention intervals confirmed previous findings of greater relative benefits for restudying in the short-term and for retesting in the long-term. Results overall support the conclusion that repeated reactivation through testing or sleeping stabilizes information against forgetting.

Retrieval as a Fast Route to Memory Consolidation.

Retrieval-mediated learning is a powerful way to make memories last, but its neurocognitive mechanisms remain unclear. We propose that retrieval acts as a rapid consolidation event, supporting the creation of adaptive hippocampal-neocortical representations via the 'online' reactivation of associative information. We describe parallels between online retrieval and offline consolidation and offer testable predictions for future research.