Memory Reactivation during Sleep Does Not Act Holistically on Object Memory.

Memory reactivation during sleep is thought to facilitate memory consolidation. Most sleep reactivation research has examined how reactivation of specific facts, objects, and associations benefits their overall retention. However, our memories are not unitary, and not all features of a memory persist in tandem over time. Instead, our memories are transformed, with some features strengthened and others weakened. Does sleep reactivation drive memory transformation? We leveraged the Targeted Memory Reactivation technique in an object category learning paradigm to examine this question. Participants (20 female, 14 male) learned three categories of novel objects, where each object had unique, distinguishing features as well as features shared with other members of its category. We used a real-time EEG protocol to cue the reactivation of these objects during sleep at moments optimized to generate reactivation events. We found that reactivation improved memory for distinguishing features while worsening memory for shared features, suggesting a differentiation process. The results indicate that sleep reactivation does not act holistically on object memories, instead supporting a transformation where some features are enhanced over others.

Maintenance of Procedural Motor Memory across Brief Rest Periods Requires the Hippocampus.

Research on the role of the hippocampus in memory acquisition has generally focused on active learning. But to understand memory, it is at least as important to understand processes that happen offline, during both wake and sleep. In a study of patients with amnesia, we previously demonstrated that although a functional hippocampus is not necessary for the acquisition of procedural motor memory during training session, it is required for its offline consolidation during sleep. Here, we investigated whether an intact hippocampus is also required for the offline consolidation of procedural motor memory while awake. Patients with amnesia due to hippocampal damage (n = 4, all male) and demographically matched controls (n = 10, 8 males) trained on the finger tapping motor sequence task. Learning was measured as gains in typing speed and was divided into online (during task execution) and offline (during interleaved 30 s breaks) components. Amnesic patients and controls showed comparable total learning, but differed in the pattern of performance improvement. Unlike younger adults, who gain speed across breaks, both groups gained speed only while typing. Only controls retained these gains over the breaks; amnesic patients slowed down and compensated for these losses during subsequent typing. In summary, unlike their peers, whose motor performance remained stable across brief breaks in typing, amnesic patients showed evidence of impaired access to motor procedural memory. We conclude that in addition to being necessary for the offline consolidation of motor memories during sleep, the hippocampus maintains access to motor memory across brief offline periods during wake.

A model of autonomous interactions between hippocampus and neocortex driving sleep-dependent memory consolidation.

How do we build up our knowledge of the world over time? Many theories of memory formation and consolidation have posited that the hippocampus stores new information, then "teaches" this information to the neocortex over time, especially during sleep. But it is unclear, mechanistically, how this actually works-How are these systems able to interact during periods with virtually no environmental input to accomplish useful learning and shifts in representation? We provide a framework for thinking about this question, with neural network model simulations serving as demonstrations. The model is composed of hippocampus and neocortical areas, which replay memories and interact with one another completely autonomously during simulated sleep. Oscillations are leveraged to support error-driven learning that leads to useful changes in memory representation and behavior. The model has a non-rapid eye movement (NREM) sleep stage, where dynamics between the hippocampus and neocortex are tightly coupled, with the hippocampus helping neocortex to reinstate high-fidelity versions of new attractors, and a REM sleep stage, where neocortex is able to more freely explore existing attractors. We find that alternating between NREM and REM sleep stages, which alternately focuses the model's replay on recent and remote information, facilitates graceful continual learning. We thus provide an account of how the hippocampus and neocortex can interact without any external input during sleep to drive useful new cortical learning and to protect old knowledge as new information is integrated.

Representations of Temporal Community Structure in Hippocampus and Precuneus Predict Inductive Reasoning Decisions.

Our understanding of the world is shaped by inferences about underlying structure. For example, at the gym, you might notice that the same people tend to arrive around the same time and infer that they are friends that work out together. Consistent with this idea, after participants are presented with a temporal sequence of objects that follows an underlying community structure, they are biased to infer that objects from the same community share the same properties. Here, we used fMRI to measure neural representations of objects after temporal community structure learning and examine how these representations support inference about object relationships. We found that community structure learning affected inferred object similarity: When asked to spatially group items based on their experience, participants tended to group together objects from the same community. Neural representations in perirhinal cortex predicted individual differences in object grouping, suggesting that high-level object representations are affected by temporal community learning. Furthermore, participants were biased to infer that objects from the same community would share the same properties. Using computational modeling of temporal learning and inference decisions, we found that inductive reasoning is influenced by both detailed knowledge of temporal statistics and abstract knowledge of the temporal communities. The fidelity of temporal community representations in hippocampus and precuneus predicted the degree to which temporal community membership biased reasoning decisions. Our results suggest that temporal knowledge is represented at multiple levels of abstraction, and that perirhinal cortex, hippocampus, and precuneus may support inference based on this knowledge.

The roles of item exposure and visualization success in the consolidation of memories across wake and sleep.

Memory consolidation during sleep does not benefit all memories equally. Initial encoding strength appears to play a role in governing where sleep effects are seen, but it is unclear whether sleep preferentially consolidates weaker or stronger memories. We manipulated encoding strength along two dimensions-the number of item presentations, and success at visualizing each item, in a sample of 82 participants. Sleep benefited memory of successfully visualized items only. Within these, the sleep-wake difference was largest for more weakly encoded information. These results suggest that the benefit of sleep on memory is seen most clearly for items that are encoded to a lower initial strength.

The hippocampus is necessary for the consolidation of a task that does not require the hippocampus for initial learning.

During sleep, the hippocampus plays an active role in consolidating memories that depend on it for initial encoding. There are hints in the literature that the hippocampus may have a broader influence, contributing to the consolidation of memories that may not initially require the area. We tested this possibility by evaluating learning and consolidation of the motor sequence task (MST) in hippocampal amnesics and demographically matched control participants. While the groups showed similar initial learning, only controls exhibited evidence of overnight consolidation. These results demonstrate that the hippocampus can be required for normal consolidation of a task without being required for its acquisition, suggesting that the area plays a broader role in coordinating memory consolidation than has previously been assumed.

Hippocampal Structure Predicts Statistical Learning and Associative Inference Abilities during Development.

Despite the importance of learning and remembering across the lifespan, little is known about how the episodic memory system develops to support the extraction of associative structure from the environment. Here, we relate individual differences in volumes along the hippocampal long axis to performance on statistical learning and associative inference tasks-both of which require encoding associations that span multiple episodes-in a developmental sample ranging from ages 6 to 30 years. Relating age to volume, we found dissociable patterns across the hippocampal long axis, with opposite nonlinear volume changes in the head and body. These structural differences were paralleled by performance gains across the age range on both tasks, suggesting improvements in the cross-episode binding ability from childhood to adulthood. Controlling for age, we also found that smaller hippocampal heads were associated with superior behavioral performance on both tasks, consistent with this region's hypothesized role in forming generalized codes spanning events. Collectively, these results highlight the importance of examining hippocampal development as a function of position along the hippocampal axis and suggest that the hippocampal head is particularly important in encoding associative structure across development.

Statistical learning of temporal community structure in the hippocampus.

The hippocampus is involved in the learning and representation of temporal statistics, but little is understood about the kinds of statistics it can uncover. Prior studies have tested various forms of structure that can be learned by tracking the strength of transition probabilities between adjacent items in a sequence. We test whether the hippocampus can learn higher-order structure using sequences that have no variance in transition probability and instead exhibit temporal community structure. We find that the hippocampus is indeed sensitive to this form of structure, as revealed by its representations, activity dynamics, and connectivity with other regions. These findings suggest that the hippocampus is a sophisticated learner of environmental regularities, able to uncover higher-order structure that requires sensitivity to overlapping associations.

Neural context reinstatement predicts memory misattribution.

What causes new information to be mistakenly attributed to an old experience? Some theories predict that reinstating the context of a prior experience allows new information to be bound to that context, leading to source memory confusion. To examine this prediction, we had human participants study two lists of items (visual objects) on separate days while undergoing functional magnetic resonance imaging. List 1 items were accompanied by a stream of scene images during the intertrial interval, but list 2 items were not. As in prior work by Hupbach et al. (2009), we observed an asymmetric pattern of misattributions on a subsequent source memory test: participants showed a strong tendency to misattribute list 2 items to list 1 but not vice versa. We hypothesized that these memory errors were due to participants reinstating the list 1 context during list 2. To test this hypothesis, we used a pattern classifier to measure scene-related neural activity during list 2 study. Because scenes were visually present during list 1 but not list 2, scene-related activity during list 2 study can be used as a time-varying neural indicator of how much participants were reinstating the list 1 context during list 2 study. In keeping with our hypothesis, we found that prestimulus scene activation during the study of list 2 items was significantly higher for items subsequently misattributed to list 1 than for items subsequently correctly attributed to list 2. We conclude by discussing how these findings relate to theories of memory reconsolidation.

The necessity of the medial temporal lobe for statistical learning.

The sensory input that we experience is highly patterned, and we are experts at detecting these regularities. Although the extraction of such regularities, or statistical learning (SL), is typically viewed as a cortical process, recent studies have implicated the medial temporal lobe (MTL), including the hippocampus. These studies have employed fMRI, leaving open the possibility that the MTL is involved but not necessary for SL. Here, we examined this issue in a case study of LSJ, a patient with complete bilateral hippocampal loss and broader MTL damage. In Experiments 1 and 2, LSJ and matched control participants were passively exposed to a continuous sequence of shapes, syllables, scenes, or tones containing temporal regularities in the co-occurrence of items. In a subsequent test phase, the control groups exhibited reliable SL in all conditions, successfully discriminating regularities from recombinations of the same items into novel foil sequences. LSJ, however, exhibited no SL, failing to discriminate regularities from foils. Experiment 3 ruled out more general explanations for this failure, such as inattention during exposure or difficulty following test instructions, by showing that LSJ could discriminate which individual items had been exposed. These findings provide converging support for the importance of the MTL in extracting temporal regularities.

Structure shapes the representation of a novel category.

Concepts contain rich structures that support flexible semantic cognition. These structures can be characterized by patterns of feature covariation: Certain features tend to cluster in the same items (e.g., feathers, wings, can fly). Existing computational models demonstrate how this kind of structure can be leveraged to slowly learn the distinctions between categories, on developmental timescales. However, it is not clear whether and how we leverage feature structure to quickly learn a novel category. We thus investigated how the internal structure of a new category is first extracted from experience, with the prediction that feature-based structure would have a rapid and broad influence on the learned category representation. Across three experiments, novel categories were designed with patterns of feature associations determined by carefully constructed graph structures, with Modular graphs-exhibiting strong clusters of feature covariation-compared against Random and Lattice graphs. In Experiment 1, a feature inference task using verbal stimuli revealed that Modular structure broadly facilitated category learning. Experiment 2 replicated this effect in visual categories. In Experiment 3, a statistical learning paradigm revealed that this Modular benefit relates to high-level structure rather than pairwise feature associations and persists even when category structure is incidental to the task. A neural network model was readily able to account for these effects, suggesting that correlational feature structure may be encoded within rapidly learned, distributed category representations. These findings constrain theories of category representation and link theories of category learning with structure learning more broadly. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Rapid learning of temporal dependencies at multiple timescales.

Our environment contains temporal information unfolding simultaneously at multiple timescales. How do we learn and represent these dynamic and overlapping information streams? We investigated these processes in a statistical learning paradigm with simultaneous short and long timescale contingencies. Human participants (N=96) played a game where they learned to quickly click on a target image when it appeared in one of 9 locations, in 8 different contexts. Across contexts, we manipulated the order of target locations: at a short timescale, the order of pairs of sequential locations in which the target appeared; at a longer timescale, the set of locations that appeared in the first vs. second half of the game. Participants periodically predicted the upcoming target location, and later performed similarity judgements comparing the games based on their order properties. Participants showed context dependent sensitivity to order information at both short and long timescales, with evidence of stronger learning for short timescales. We modeled the learning paradigm using a gated recurrent network trained to make immediate predictions, which demonstrated multilevel learning timecourses and patterns of sensitivity to the similarity structure of the games that mirrored human participants. The model grouped games with matching rule structure and dissociated games based on low-level order information more so than high-level order information. The work shows how humans and models can rapidly and concurrently acquire order information at different timescales.

Why bilateral damage is worse than unilateral damage to the brain.

Human and animal lesion studies have shown that behavior can be catastrophically impaired after bilateral lesions but that unilateral damage often produces little or no effect, even controlling for lesion extent. This pattern is found across many different sensory, motor, and memory domains. Despite these findings, there has been no systematic, computational explanation. We found that the same striking difference between unilateral and bilateral damage emerged in a distributed, recurrent attractor neural network. The difference persists in simple feedforward networks, where it can be understood in explicit quantitative terms. In essence, damage both distorts and reduces the magnitude of relevant activity in each hemisphere. Unilateral damage reduces the relative magnitude of the contribution to performance of the damaged side, allowing the intact side to dominate performance. In contrast, balanced bilateral damage distorts representations on both sides, which contribute equally, resulting in degraded performance. The model's ability to account for relevant patient data suggests that mechanisms similar to those in the model may operate in the brain.

A neural network model of hippocampal contributions to category learning.

In addition to its critical role in encoding individual episodes, the hippocampus is capable of extracting regularities across experiences. This ability is central to category learning, and a growing literature indicates that the hippocampus indeed makes important contributions to this form of learning. Using a neural network model that mirrors the anatomy of the hippocampus, we investigated the mechanisms by which the hippocampus may support novel category learning. We simulated three category learning paradigms and evaluated the network's ability to categorize and recognize specific exemplars in each. We found that the trisynaptic pathway within the hippocampus-connecting entorhinal cortex to dentate gyrus, CA3, and CA1-was critical for remembering exemplar-specific information, reflecting the rapid binding and pattern separation capabilities of this circuit. The monosynaptic pathway from entorhinal cortex to CA1, in contrast, specialized in detecting the regularities that define category structure across exemplars, supported by the use of distributed representations and a relatively slower learning rate. Together, the simulations provide an account of how the hippocampus and its constituent pathways support novel category learning.

Effect of sleep manipulations on intrusive memories after exposure to an experimental analogue trauma: A meta-analytic review.

Sleep plays an important role in memory processing and is disrupted in individuals with post-traumatic stress disorder (PTSD). A growing body of research has experimentally investigated how sleep - or lack thereof - in the early aftermath of a traumatic experience contributes to intrusive memory formation. The aim of this meta-analytic review was to examine the effects of various experimental sleep manipulations (e.g., sleep deprivation, daytime naps) on intrusive memories following exposure to an experimentally induced analogue traumatic event. Eight eligible studies were systematically identified through PsycInfo and PubMed and provided sufficient data to contribute to a meta-analysis of the effects of sleep versus wakefulness on intrusive memory frequency. Sleep was found to reduce intrusive memory frequency when compared to wakefulness at a small but significant effect size (Hedge's g = 0.29). There was no evidence of publication bias and heterogeneity of effect sizes across studies was moderate. Results suggest that sleep plays a protective role in the aftermath of exposure to a traumatic event with implications for early post-trauma intervention efforts.

Building integrated representations through interleaved learning.

Inferring relationships that go beyond our direct experience is essential for understanding our environment. This capacity requires either building representations that directly reflect structure across experiences as we encounter them or deriving the indirect relationships across experiences as the need arises. Building structure directly into overlapping representations allows for powerful learning and generalization in neural network models, but building these so-called distributed representations requires inputs to be encountered in interleaved order. We test whether interleaving similarly facilitates the formation of representations that directly integrate related experiences in humans and what advantages such integration may confer for behavior. In a series of behavioral experiments, we present evidence that interleaved learning indeed promotes the formation of representations that directly link across related experiences. As in neural network models, interleaved learning gives rise to fast and automatic recognition of item relatedness, affords efficient generalization, and is especially critical for inference when learning requires statistical integration of noisy information over time. We use the data to adjudicate between several existing computational models of human memory and inference. The results demonstrate the power of interleaved learning and implicate the formation of integrated, distributed representations that support generalization in humans. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Emotional Faces Facilitate Statistical Learning.

Detecting regularities and extracting patterns is a vital skill to organize complex information in our environments. Statistical learning, a process where we detect regularities by attending to relationships between cues in our environment, contributes to knowledge acquisition across myriad domains. However, less is known about how emotional cues-specifically facial configurations of emotion-influence statistical learning. Here, we tested two pre-registered aims to advance knowledge about emotional signals and statistical learning: (1) we examined statistical learning in the context of emotional compared to non-emotional information, and (2) we assessed how emotional congruency (i.e., whether facial stimuli conveyed the same, or different emotions) influenced regularity extraction. We demonstrated statistical learning in the context of emotional signals. Further, we showed that statistical learning occurs more efficiently in the context of emotional faces. We also established that congruent cues benefited an online measure of statistical learning, but had varied effects when statistical learning was assessed via post-exposure recognition test. The results shed light on how affective signals influence well-studied cognitive skills and address a knowledge gap about how cue congruency impacts statistical learning, including how emotional cues might guide predictions in our social world. Supplementary Information: The online version contains supplementary material available at 10.1007/s42761-022-00130-9.

Tracking the relation between gist and item memory over the course of long-term memory consolidation.

Our experiences in the world support memories not only of specific episodes but also of the generalities (the 'gist') across related experiences. It remains unclear how these two types of memories evolve and influence one another over time. In two experiments, 173 human participants encoded spatial locations from a distribution and reported both item memory (specific locations) and gist memory (center for the locations) across 1-2 months. Experiment 1 demonstrated that after 1 month, gist memory was preserved relative to item memory, despite a persistent positive correlation between them. Critically, item memories were biased toward the gist over time. Experiment 2 showed that a spatial outlier item changed this relationship and that the extraction of gist is sensitive to the regularities of items. Our results suggest that the gist starts to guide item memories over longer durations as their relative strengths change.

Memory consolidation as an adaptive process.

We rely on our long-term memories to guide future behaviors, making it adaptive to prioritize the retention of goal-relevant, salient information in memory. In this review, we discuss findings from rodent and human research to demonstrate that active processes during post-encoding consolidation support the selective stabilization of recent experience into adaptive, long-term memories. Building upon literatures focused on dynamics at the cellular level, we highlight that consolidation also transforms memories at the systems level to support future goal-relevant behavior, resulting in more generalized memory traces in the brain and behavior. We synthesize previous literatures spanning animal research, human cognitive neuroscience, and cognitive psychology to propose an integrative framework for adaptive consolidation by which goal-relevant memoranda are "tagged" for subsequent consolidation, resulting in selective transformations to the structure of memories that support flexible, goal-relevant behaviors.