Neural Differentiation of Incorrectly Predicted Memories

Finding the sweet spot of memory modification: An fMRI study on episodic prediction error strength and type

Previous research has highlighted the critical role of prediction errors (PEs) in signaling the need to adapt memory representations in response to unexpected changes in the environment. Yet, the influence of PE type and strength on memory remains underexplored. In this study, participants encoded naturalistic dialogues prior to undergoing fMRI scanning. During the fMRI session, they listened to dialogues that had been modified in their surface or gist, to varying extents. As expected, our findings revealed robust activation in the inferior frontal gyrus for all PEs. Notably, gist modifications elicited additional activations within the episodic memory network, including the hippocampus. A post-fMRI recognition test demonstrated that surface modifications had no significant impact on memory. Conversely, weak gist changes impaired memory for the original content and hindered learning of the modification. These weak gist changes also triggered activation in the parahippocampal cortex. These results underscore the importance of both the type and strength of PEs in shaping brain activations and memory outcomes, highlighting their complex interplay in cognitive processes.

Memory updating and the structure of event representations

People form memories of specific events and use those memories to make predictions about similar new experiences. Living in a dynamic environment presents a challenge: How does one represent valid prior events in memory while encoding new experiences when things change? There is evidence for two seemingly contradictory classes of mechanism: One differentiates outdated event features by making them less similar or less accessible than updated event features. The other integrates updated features of new events with outdated memories, and the relationship between them, into a structured representation. Integrative encoding may occur when changed events trigger inaccurate predictions based on remembered prior events. We propose that this promotes subsequent recollection of events and their order, enabling adaptation to environmental changes.

The neural basis of the insight memory advantage

Creative problem solving and memory are inherently intertwined: memory accesses existing knowledge while creativity enhances it. Recent studies show that insights often accompanying creative solutions enhance long-term memory. This insight memory advantage (IMA) is explained by the 'insight as prediction error (PE)' hypothesis which states that insights arise from PEs updating predictive solution models and thereby enhancing memory. Neurally, the hippocampus initially detects PEs and then, together with the medial prefrontal cortex (mPFC), integrates and updates these expectations facilitating efficient memory encoding and retrieval. Dopamine (DA) mediates reward PEs and long-term potentiation (LTP) in the hippocampus, while noradrenaline (NE) enhances arousal and attention impacting the amygdala, the salience network, and hippocampal plasticity. These neurobiological mechanisms likely underpin IMA and have significant implications for educational practices and problem-solving strategies.

Inducing representational change in the hippocampus through real-time neurofeedback

When you perceive or remember something, other related things come to mind, affecting how these competing items are subsequently perceived and remembered. Such behavioural consequences are believed to result from changes in the overlap of neural representations of these items, especially in the hippocampus. According to multiple theories, hippocampal overlap should increase (integration) when there is high coactivation between cortical representations. However, prior studies used indirect proxies for coactivation by manipulating stimulus similarity or task demands. Here, we induce coactivation in visual cortex more directly using closed-loop neurofeedback from real-time functional magnetic resonance imaging (fMRI). While viewing one object, participants were rewarded for activating the representation of another object as strongly as possible. Across multiple real-time fMRI sessions, participants succeeded in using this neurofeedback to increase coactivation. Compared with a baseline of untrained objects, this protocol led to memory integration in behaviour and the brain: the trained objects became harder for participants to discriminate behaviourally in a categorical perception task and harder to discriminate neurally from patterns of fMRI activity in their hippocampus as a result of losing unique features. These findings demonstrate that neurofeedback can be used to alter and combine memories.This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

Reconciling category exceptions through representational shifts

Real-world categories often contain exceptions that disobey the perceptual regularities followed by other members. Prominent psychological and neurobiological theories indicate that exception learning relies on the flexible modulation of object representations, but the specific representational shifts key to learning remain poorly understood. Here, we leveraged behavioral and computational approaches to elucidate the representational dynamics during the acquisition of exceptions that violate established regularity knowledge. In our study, participants (n = 42) learned novel categories in which regular and exceptional items were introduced successively; we then fitted a computational model to individuals' categorization performance to infer latent stimulus representations before and after exception learning. We found that in the representational space, exception learning not only drove confusable exceptions to be differentiated from regular items, but also led exceptions within the same category to be integrated based on shared characteristics. These shifts resulted in distinct representational clusters of regular items and exceptions that constituted hierarchically structured category representations, and the distinct clustering of exceptions from regular items was associated with a high ability to generalize and reconcile knowledge of regularities and exceptions. Moreover, by having a second group of participants (n = 42) to judge stimuli's similarity before and after exception learning, we revealed misalignment between representational similarity and behavioral similarity judgments, which further highlights the hierarchical layouts of categories with regularities and exceptions. Altogether, our findings elucidate the representational dynamics giving rise to generalizable category structures that reconcile perceptually inconsistent category members, thereby advancing the understanding of knowledge formation.

The role of REM sleep in neural differentiation of memories in the hippocampus

When faced with a familiar situation, we can use memory to make predictions about what will happen next. If such predictions turn out to be erroneous, the brain can adapt by differentiating the representations of the cues that generated the prediction from the mispredicted item itself, reducing the likelihood of future prediction errors. Prior work by Kim et al. (2017) found that violating a sequential association in a statistical learning paradigm triggered differentiation of the neural representations of the associated items in the hippocampus. Here, we used fMRI to test the preregistered hypothesis that this hippocampal differentiation occurs only when violations are followed by rapid eye movement (REM) sleep. In the morning, participants first learned that some items predict others (e.g., A predicts B) then encountered a violation in which a predicted item (B) failed to appear when expected after its associated item (A); the predicted item later appeared on its own after an unrelated item. Participants were then randomly assigned to one of three conditions: remain awake, take a nap containing non-REM sleep only, or take a nap with both non-REM and REM sleep. While the predicted results were not observed in the preregistered left CA2/3/DG ROI, we did observe evidence for our hypothesis in closely related hippocampal ROIs, uncorrected for multiple comparisons: In right CA2/3/DG, differentiation in the group with REM sleep was greater than in the groups without REM sleep (wake and non-REM nap); this differentiation was item-specific and concentrated in right DG. Differentiation effects were also greater in bilateral DG when the predicted item was more strongly reactivated during the violation. Overall, the results presented here provide initial evidence linking REM sleep to changes in the hippocampal representations of memories in humans.

Time after Time: Preserving Temporal Memories When Experiences Repeat

Remembering when events occur in time is fundamental to episodic memory. Yet, many experiences repeat over time creating the potential for interference when attempting to recall temporally specific memories. Here, we argue that temporal memories are protected, in part, by reinstatement of temporal context information that is triggered by stimulus repetitions. We motivate this argument by integrating seminal findings across several distinct literatures and methodologies. Specifically, we consider key insights from foundational behavioral studies of temporal memory, recent electrophysiological and neuroimaging approaches to measuring memory reinstatement, and computational models that describe how temporal context representations shape memory processes. We also note several open questions concerning how temporal context reinstatement might influence subsequent temporal memory, including potential mediating effects of event spacing and event boundaries. These ideas and questions have the potential to guide future research and, ultimately, to advance theoretical accounts of how we preserve temporal memories.

Repulsion of CA3 / dentate gyrus representations is driven by distinct internal beliefs in the face of ambiguous sensory input

Recent human neuroimaging studies of episodic memory have revealed a counterintuitive phenomenon in the hippocampus: when events are highly similar, corresponding hippocampal activity patterns are sometimes less correlated than activity patterns associated with unrelated events. This phenomenon-repulsion-is not accounted for by most theories of the hippocampus, and the conditions that trigger repulsion remain poorly understood. Here, we used a spatial route-learning task and high-resolution fMRI in humans to test whether hippocampal repulsion is fundamentally driven by internal beliefs about the environment. By precisely measuring participants' internal beliefs and actively manipulating them, we show that repulsion selectively occurred in hippocampal subfields CA3 and dentate gyrus when visual input was ambiguous-or even identical-but internal beliefs were distinct. These findings firmly establish conditions that elicit repulsion and have broad relevance to theories of hippocampal function and to the fields of human episodic memory and rodent spatial navigation.

A neural network model of differentiation and integration of competing memories

What determines when neural representations of memories move together (integrate) or apart (differentiate)? Classic supervised learning models posit that, when two stimuli predict similar outcomes, their representations should integrate. However, these models have recently been challenged by studies showing that pairing two stimuli with a shared associate can sometimes cause differentiation, depending on the parameters of the study and the brain region being examined. Here, we provide a purely unsupervised neural network model that can explain these and other related findings. The model can exhibit integration or differentiation depending on the amount of activity allowed to spread to competitors - inactive memories are not modified, connections to moderately active competitors are weakened (leading to differentiation), and connections to highly active competitors are strengthened (leading to integration). The model also makes several novel predictions - most importantly, that when differentiation occurs as a result of this unsupervised learning mechanism, it will be rapid and asymmetric, and it will give rise to anticorrelated representations in the region of the brain that is the source of the differentiation. Overall, these modeling results provide a computational explanation for a diverse set of seemingly contradictory empirical findings in the memory literature, as well as new insights into the dynamics at play during learning.

Neural Activity Differentiates Novel and Learned Event Boundaries

People parse continuous experiences at natural breakpoints called event boundaries, which is important for understanding an environment's causal structure and for responding to uncertainty within it. However, it remains unclear how different forms of uncertainty affect the parsing of continuous experiences and how such uncertainty influences the brain's processing of ongoing events. We exposed human participants of both sexes (N = 34) to a continuous sequence of semantically meaningless images. We generated sequences from random walks through a graph that grouped images into temporal communities. After learning, we asked participants to segment another sequence at natural breakpoints (event boundaries). Participants segmented the sequence at learned transitions between communities, as well as at novel transitions, suggesting that people can segment temporally extended experiences into events based on learned structure as well as prediction error. Greater segmentation at novel boundaries was associated with enhanced parietal scalp electroencephalography (EEG) activity between 250 and 450 ms after the stimulus onset. Multivariate classification of EEG activity showed that novel and learned boundaries evoked distinct patterns of neural activity, particularly theta band power in posterior electrodes. Learning also led to distinct neural representations for stimuli within the temporal communities, while neural activity at learned boundary nodes showed predictive evidence for the adjacent community. The data show that people segment experiences at both learned and novel boundaries and suggest that learned event boundaries trigger retrieval of information about the upcoming community that could underlie anticipation of the next event in a sequence.

Event boundaries drive norepinephrine release and distinctive neural representations of space in the rodent hippocampus

Episodic memories are temporally segmented around event boundaries that tend to coincide with moments of environmental change. During these times, the state of the brain should change rapidly, or reset, to ensure that the information encountered before and after an event boundary is encoded in different neuronal populations. Norepinephrine (NE) is thought to facilitate this network reorganization. However, it is unknown whether event boundaries drive NE release in the hippocampus and, if so, how NE release relates to changes in hippocampal firing patterns. The advent of the new GRABNE sensor now allows for the measurement of NE binding with sub-second resolution. Using this tool in mice, we tested whether NE is released into the dorsal hippocampus during event boundaries defined by unexpected transitions between spatial contexts and presentations of novel objections. We found that NE binding dynamics were well explained by the time elapsed after each of these environmental changes, and were not related to conditioned behaviors, exploratory bouts of movement, or reward. Familiarity with a spatial context accelerated the rate in which phasic NE binding decayed to baseline. Knowing when NE is elevated, we tested how hippocampal coding of space differs during these moments. Immediately after context transitions we observed relatively unique patterns of neural spiking which settled into a modal state at a similar rate in which NE returned to baseline. These results are consistent with a model wherein NE release drives hippocampal representations away from a steady-state attractor. We hypothesize that the distinctive neural codes observed after each event boundary may facilitate long-term memory and contribute to the neural basis for the primacy effect.

Differentiation and Integration of Competing Memories: A Neural Network Model

What determines when neural representations of memories move together (integrate) or apart (differentiate)? Classic supervised learning models posit that, when two stimuli predict similar outcomes, their representations should integrate. However, these models have recently been challenged by studies showing that pairing two stimuli with a shared associate can sometimes cause differentiation, depending on the parameters of the study and the brain region being examined. Here, we provide a purely unsupervised neural network model that can explain these and other related findings. The model can exhibit integration or differentiation depending on the amount of activity allowed to spread to competitors - inactive memories are not modified, connections to moderately active competitors are weakened (leading to differentiation), and connections to highly active competitors are strengthened (leading to integration). The model also makes several novel predictions - most importantly, that when differentiation occurs as a result of this unsupervised learning mechanism, it will be rapid and asymmetric, and it will give rise to anticorrelated representations in the region of the brain that is the source of the differentiation. Overall, these modeling results provide a computational explanation for a diverse set of seemingly contradictory empirical findings in the memory literature, as well as new insights into the dynamics at play during learning.

Event Integration and Temporal Differentiation: How Hierarchical Knowledge Emerges in Hippocampal Subfields through Learning

Everyday life is composed of events organized by changes in contexts, with each event containing an unfolding sequence of occurrences. A major challenge facing our memory systems is how to integrate sequential occurrences within events while also maintaining their details and avoiding over-integration across different contexts. We asked if and how distinct hippocampal subfields come to hierarchically and, in parallel, represent both event context and subevent occurrences with learning. Female and male human participants viewed sequential events defined as sequences of objects superimposed on shared color frames while undergoing high-resolution fMRI. Importantly, these events were repeated to induce learning. Event segmentation, as indexed by increased reaction times at event boundaries, was observed in all repetitions. Temporal memory decisions were quicker for items from the same event compared to across different events, indicating that events shaped memory. With learning, hippocampal CA3 multivoxel activation patterns clustered to reflect the event context, with more clustering correlated with behavioral facilitation during event transitions. In contrast, in the dentate gyrus (DG), temporally proximal items that belonged to the same event became associated with more differentiated neural patterns. A computational model explained these results by dynamic inhibition in the DG. Additional similarity measures support the notion that CA3 clustered representations reflect shared voxel populations, while DG's distinct item representations reflect different voxel populations. These findings suggest an interplay between temporal differentiation in the DG and attractor dynamics in CA3. They advance our understanding of how knowledge is structured through integration and separation across time and context.

Multiple Memory Subsystems: Reconsidering Memory in the Mind and Brain

The multiple-memory-systems framework-that distinct types of memory are supported by distinct brain systems-has guided learning and memory research for decades. However, recent work challenges the one-to-one mapping between brain structures and memory types central to this taxonomy, with key memory-related structures supporting multiple functions across substructures. Here we integrate cross-species findings in the hippocampus, striatum, and amygdala to propose an updated framework of multiple memory subsystems (MMSS). We provide evidence for two organizational principles of the MMSS theory: First, opposing memory representations are colocated in the same brain structures; second, parallel memory representations are supported by distinct structures. We discuss why this burgeoning framework has the potential to provide a useful revision of classic theories of long-term memory, what evidence is needed to further validate the framework, and how this novel perspective on memory organization may guide future research.

INDUCING REPRESENTATIONAL CHANGE IN THE HIPPOCAMPUS THROUGH REAL-TIME NEUROFEEDBACK

When you perceive or remember one thing, other related things come to mind. This competition has consequences for how these items are later perceived, attended, or remembered. Such behavioral consequences result from changes in how much the neural representations of the items overlap, especially in the hippocampus. These changes can reflect increased (integration) or decreased (differentiation) overlap; previous studies have posited that the amount of coactivation between competing representations in cortex determines which will occur: high coactivation leads to hippocampal integration, medium coactivation leads to differentiation, and low coactivation is inert. However, those studies used indirect proxies for coactivation, by manipulating stimulus similarity or task demands. Here we induce coactivation of competing memories in visual cortex more directly using closed-loop neurofeedback from real-time fMRI. While viewing one object, participants were rewarded for implicitly activating the representation of another object as strongly as possible. Across multiple real-time fMRI training sessions, they succeeded in using the neurofeedback to induce coactivation. Compared with untrained objects, this coactivation led to behavioral and neural integration: The trained objects became harder for participants to discriminate in a categorical perception task and harder to decode from patterns of fMRI activity in the hippocampus.

False memories for ending of events

Memories are not perfect recordings of the past and can be subject to systematic biases. Memory distortions are often caused by our experience of what typically happens in a given situation. However, it is unclear whether memory for events is biased by the knowledge that events usually have a predictable structure (a beginning, middle, and an end). Using video clips of everyday situations, we tested how interrupting events at unexpected time points affects memory of how those events ended. In four free recall experiments (1, 2, 4, and 5), we found that interrupting clips just before a salient piece of action was completed, resulted in the false recall of details about how the clip might have ended. We refer to this as "event extension." On the other hand, interrupting clips just after one scene had ended and a new scene started, resulted in omissions of details about the true ending of the clip (Experiments 4 and 5). We found that these effects were present, albeit attenuated, when testing memory shortly after watching the video clips compared to a week later (Experiments 5a and 5b). The event extension effect was not present when memory was tested with a recognition paradigm (Experiment 3). Overall, we conclude that when people watch videos that violate their expectations of typical event structure, they show a bias to later recall the videos as if they had ended at a predictable event boundary, exhibiting event extension or the omission of details depending on where the original video was interrupted. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Prediction error and memory across the lifespan

The influence of Prediction Errors (PEs) on episodic memory has generated growing empirical and theoretical interest. This review explores how the relationship between PE and memory may evolve throughout lifespan. Drawing upon the predictive processing framework and the Predictive, Interactive Multiple Memory System (PIMMS) model in particular, the paper highlights the hierarchical organization of memory systems and the interaction between top-down predictions and bottom-up sensory input, proposing that PEs promote synaptic change and improve encoding and consolidation processes. We discuss the neuroscientific mechanisms underlying PE-driven memory enhancement, focusing on the involvement of the hippocampus, the entorhinal cortex-hippocampus pathway, and the noradrenergic sympathetic system. Recognizing the divergent trajectories of episodic and semantic memory across the lifespan is crucial when examining the effects of PEs on memory. This review underscores the heterogeneity of memory processes and neurocognitive mechanisms underlying PE-driven memory enhancement across age. Future research is suggested to directly compare neural networks involved in learning from PEs across different age groups and to contribute to a deeper understanding of PE-driven learning across age.

Predictions transform memories: How expected versus unexpected events are integrated or separated in memory

Our brains constantly generate predictions about the environment based on prior knowledge. Many of the events we experience are consistent with these predictions, while others might be inconsistent with prior knowledge and thus violate our predictions. To guide future behavior, the memory system must be able to strengthen, transform, or add to existing knowledge based on the accuracy of our predictions. We synthesize recent evidence suggesting that when an event is consistent with our predictions, it leads to neural integration between related memories, which is associated with enhanced associative memory, as well as memory biases. Prediction errors, in turn, can promote both neural integration and separation, and lead to multiple mnemonic outcomes. We review these findings and how they interact with factors such as memory reactivation, prediction error strength, and task goals, to offer insight into what determines memory for events that violate our predictions. In doing so, this review brings together recent neural and behavioral research to advance our understanding of how predictions shape memory, and why.

Adult age differences in event memory updating: The roles of prior-event retrieval and prediction

Remembering past events can lead to predictions of what is to come and to experiencing prediction errors when things change. Previous research has shown enhanced memory updating for ongoing events that are inconsistent with predictions based on past experiences. According to the Event Memory Retrieval and Comparison (EMRC) Theory, such memory updating depends on the encoding of configural representations that bind retrieved features of the previous event, changed features, and the relationship between the two. We investigated potential age-related differences in these mechanisms by showing older and younger adults two movies of everyday activities. Activities in the second movie either repeated from the first movie or included changed endings. During the second movie, before activities ended, participants were instructed to predict the upcoming action based on the first movie. One week later, participants were instructed to recall activity endings from the second movie. For younger adults, having predicted endings consistent with the first movie before seeing changed endings was subsequently associated with better recall of these changed endings and recollection that activities had changed. Conversely, for older adults, making such predictions prior to changes was associated with intruding details from the first movie endings and was less strongly associated with change recollection. Consistent with EMRC, these findings suggest that retrieval of relevant experiences when events change can trigger prediction errors that prompt associative encoding of existing memories and current perceptions. These mechanisms were less efficient in older adults, which may account for their poorer event memory updating than younger adults. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Adaptive expression of engrams by retroactive interference

Long-term memories are stored as configurations of neuronal ensembles, termed engrams. Although investigation of engram cell properties and functionality in memory recall has been extensive, less is known about how engram cells are affected by forgetting. We describe a form of interference-based forgetting using an object memory behavioral paradigm. By using activity-dependent cell labeling, we show that although retroactive interference results in decreased engram cell reactivation during recall trials, optogenetic stimulation of the labeled engram cells is sufficient to induce memory retrieval. Forgotten engrams may be reinstated via the presentation of similar or related environmental information. Furthermore, we demonstrate that engram activity is necessary for interference to occur. Taken together, these findings indicate that retroactive interference modules engram expression in a manner that is both reversible and updatable. Inference may constitute a form of adaptive forgetting where, in everyday life, new perceptual and environmental inputs modulate the natural forgetting process.

Mapping multidimensional content representations to neural and behavioral expressions of episodic memory

Human neuroimaging studies have shown that the contents of episodic memories are represented in distributed patterns of neural activity. However, these studies have mostly been limited to decoding simple, unidimensional properties of stimuli. Semantic encoding models, in contrast, offer a means for characterizing the rich, multidimensional information that comprises episodic memories. Here, we extensively sampled four human fMRI subjects to build semantic encoding models and then applied these models to reconstruct content from natural scene images as they were viewed and recalled from memory. First, we found that multidimensional semantic information was successfully reconstructed from activity patterns across visual and lateral parietal cortices, both when viewing scenes and when recalling them from memory. Second, whereas visual cortical reconstructions were much more accurate when images were viewed versus recalled from memory, lateral parietal reconstructions were comparably accurate across visual perception and memory. Third, by applying natural language processing methods to verbal recall data, we showed that fMRI-based reconstructions reliably matched subjects' verbal descriptions of their memories. In fact, reconstructions from ventral temporal cortex more closely matched subjects' own verbal recall than other subjects' verbal recall of the same images. Fourth, encoding models reliably transferred across subjects: memories were successfully reconstructed using encoding models trained on data from entirely independent subjects. Together, these findings provide evidence for successful reconstructions of multidimensional and idiosyncratic memory representations and highlight the differential sensitivity of visual cortical and lateral parietal regions to information derived from the external visual environment versus internally-generated memories.

Generalization of cognitive maps across space and time

Prominent theories posit that associative memory structures, known as cognitive maps, support flexible generalization of knowledge across cognitive domains. Here, we evince a representational account of cognitive map flexibility by quantifying how spatial knowledge formed one day was used predictively in a temporal sequence task 24 hours later, biasing both behavior and neural response. Participants learned novel object locations in distinct virtual environments. After learning, hippocampus and ventromedial prefrontal cortex (vmPFC) represented a cognitive map, wherein neural patterns became more similar for same-environment objects and more discriminable for different-environment objects. Twenty-four hours later, participants rated their preference for objects from spatial learning; objects were presented in sequential triplets from either the same or different environments. We found that preference response times were slower when participants transitioned between same- and different-environment triplets. Furthermore, hippocampal spatial map coherence tracked behavioral slowing at the implicit sequence transitions. At transitions, predictive reinstatement of virtual environments decreased in anterior parahippocampal cortex. In the absence of such predictive reinstatement after sequence transitions, hippocampus and vmPFC responses increased, accompanied by hippocampal-vmPFC functional decoupling that predicted individuals' behavioral slowing after a transition. Collectively, these findings reveal how expectations derived from spatial experience generalize to support temporal prediction.

Representational integration and differentiation in the human hippocampus following goal-directed navigation

As we learn, dynamic memory processes build structured knowledge across our experiences. Such knowledge enables the formation of internal models of the world that we use to plan, make decisions, and act. Recent theorizing posits that mnemonic mechanisms of differentiation and integration - which at one level may seem to be at odds - both contribute to the emergence of structured knowledge. We tested this possibility using fMRI as human participants learned to navigate within local and global virtual environments over the course of 3 days. Pattern similarity analyses on entorhinal cortical and hippocampal patterns revealed evidence that differentiation and integration work concurrently to build local and global environmental representations, and that variability in integration relates to differences in navigation efficiency. These results offer new insights into the neural machinery and the underlying mechanisms that translate experiences into structured knowledge that allows us to navigate to achieve goals.

Representations of Complex Contexts: A Role for Hippocampus

The hippocampus plays a critical role in supporting episodic memory, in large part by binding together experiences and items with surrounding contextual information. At present, however, little is known about the roles of different hippocampal subfields in supporting this item-context binding. To address this question, we constructed a task in which items were affiliated with differing types of context-cognitive associations that vary at the local, item level and membership in temporally organized lists that linked items together at a global level. Participants made item recognition judgments while undergoing high-resolution fMRI. We performed voxel pattern similarity analyses to answer the question of how human hippocampal subfields represent retrieved information about cognitive states and the time at which a past event took place. As participants recollected previously presented items, activity patterns in the CA23DG subregion carried information about prior cognitive states associated with these items. We found no evidence to suggest reinstatement of information about temporal context at the level of list membership, but exploratory analyses revealed representations of temporal context at a coarse level in conjunction with representations of cognitive contexts. Results are consistent with characterizations of CA23DG as a critical site for binding together items and contexts in the service of memory retrieval.

Phase separation of competing memories along the human hippocampal theta rhythm

Competition between overlapping memories is considered one of the major causes of forgetting, and it is still unknown how the human brain resolves such mnemonic conflict. In the present magnetoencephalography (MEG) study, we empirically tested a computational model that leverages an oscillating inhibition algorithm to minimise overlap between memories. We used a proactive interference task, where a reminder word could be associated with either a single image (non-competitive condition) or two competing images, and participants were asked to always recall the most recently learned word-image association. Time-resolved pattern classifiers were trained to detect the reactivated content of target and competitor memories from MEG sensor patterns, and the timing of these neural reactivations was analysed relative to the phase of the dominant hippocampal 3 Hz theta oscillation. In line with our pre-registered hypotheses, target and competitor reactivations locked to different phases of the hippocampal theta rhythm after several repeated recalls. Participants who behaviourally experienced lower levels of interference also showed larger phase separation between the two overlapping memories. The findings provide evidence that the temporal segregation of memories, orchestrated by slow oscillations, plays a functional role in resolving mnemonic competition by separating and prioritising relevant memories under conditions of high interference.

A neural network model of when to retrieve and encode episodic memories

Recent human behavioral and neuroimaging results suggest that people are selective in when they encode and retrieve episodic memories. To explain these findings, we trained a memory-augmented neural network to use its episodic memory to support prediction of upcoming states in an environment where past situations sometimes reoccur. We found that the network learned to retrieve selectively as a function of several factors, including its uncertainty about the upcoming state. Additionally, we found that selectively encoding episodic memories at the end of an event (but not mid-event) led to better subsequent prediction performance. In all of these cases, the benefits of selective retrieval and encoding can be explained in terms of reducing the risk of retrieving irrelevant memories. Overall, these modeling results provide a resource-rational account of why episodic retrieval and encoding should be selective and lead to several testable predictions.

Increasing stimulus similarity drives nonmonotonic representational change in hippocampus

Studies of hippocampal learning have obtained seemingly contradictory results, with manipulations that increase coactivation of memories sometimes leading to differentiation of these memories, but sometimes not. These results could potentially be reconciled using the nonmonotonic plasticity hypothesis, which posits that representational change (memories moving apart or together) is a U-shaped function of the coactivation of these memories during learning. Testing this hypothesis requires manipulating coactivation over a wide enough range to reveal the full U-shape. To accomplish this, we used a novel neural network image synthesis procedure to create pairs of stimuli that varied parametrically in their similarity in high-level visual regions that provide input to the hippocampus. Sequences of these pairs were shown to human participants during high-resolution fMRI. As predicted, learning changed the representations of paired images in the dentate gyrus as a U-shaped function of image similarity, with neural differentiation occurring only for moderately similar images.

Prediction errors disrupt hippocampal representations and update episodic memories

The brain supports adaptive behavior by generating predictions, learning from errors, and updating memories to incorporate new information. Prediction error, or surprise, triggers learning when reality contradicts expectations. Prior studies have shown that the hippocampus signals prediction errors, but the hypothesized link to memory updating has not been demonstrated. In a human functional MRI study, we elicited mnemonic prediction errors by interrupting familiar narrative videos immediately before the expected endings. We found that prediction errors reversed the relationship between univariate hippocampal activation and memory: greater hippocampal activation predicted memory preservation after expected endings, but memory updating after surprising endings. In contrast to previous studies, we show that univariate activation was insufficient for understanding hippocampal prediction error signals. We explain this surprising finding by tracking both the evolution of hippocampal activation patterns and the connectivity between the hippocampus and neuromodulatory regions. We found that hippocampal activation patterns stabilized as each narrative episode unfolded, suggesting sustained episodic representations. Prediction errors disrupted these sustained representations and the degree of disruption predicted memory updating. The relationship between hippocampal activation and subsequent memory depended on concurrent basal forebrain activation, supporting the idea that cholinergic modulation regulates attention and memory. We conclude that prediction errors create conditions that favor memory updating, prompting the hippocampus to abandon ongoing predictions and make memories malleable.

Mnemonic prediction errors promote detailed memories

When our experience violates our predictions, it is adaptive to update our knowledge to promote a more accurate representation of the world and facilitate future predictions. Theoretical models propose that these mnemonic prediction errors should be encoded into a distinct memory trace to prevent interference with previous, conflicting memories. We investigated this proposal by repeatedly exposing participants to pairs of sequentially presented objects (A → B), thus evoking expectations. Then, we violated participants' expectations by replacing the second object in the pairs with a novel object (A → C). The following item memory test required participants to discriminate between identical old items and similar lures, thus testing detailed and distinctive item memory representations. In two experiments, mnemonic prediction errors enhanced item memory: Participants correctly identified more old items as old when those items violated expectations during learning, compared with items that did not violate expectations. This memory enhancement for C items was only observed when participants later showed intact memory for the related A → B pairs, suggesting that strong predictions are required to facilitate memory for violations. Following up on this, a third experiment reduced prediction strength prior to violation and subsequently eliminated the memory advantage of violations. Interestingly, mnemonic prediction errors did not increase gist-based mistakes of identifying old items as similar lures or identifying similar lures as old. Enhanced item memory in the absence of gist-based mistakes suggests that violations enhanced memory for items' details, which could be mediated via distinct memory traces. Together, these results advance our knowledge of how mnemonic prediction errors promote memory formation.

Examining the transition of novel information toward familiarity

Throughout their lives, humans encounter multiple instances of new information that can be inconsistent with prior knowledge (novel). Over time, the once-novel information becomes integrated into their established knowledge base, shifting from novelty to familiarity. In this study, we investigated the processes by which the first steps of this transition take place. We hypothesized that the neural representations of initially novel items gradually change over the course of repeated presentations, expressing a shift toward familiarity. We further assumed that this shift could be traced by examining neural patterns using fMRI. In two experiments, while being scanned, participants read noun-adjective word pairs that were either consistent or inconsistent with their prior knowledge. Stimuli were repeated 3-6 times within the scans. Employing mass univariate and multivariate similarity analyses, we showed that the neural representations associated with the initial presentation of familiar versus novel objects differed in lateral frontal and temporal regions, the medial prefrontal cortex, and the medial temporal lobe. Importantly, the neural representations of novel stimuli gradually changed throughout repetitions until they became indistinguishable from their respective familiar items. We interpret these findings as indicating that an early phase of familiarization can be completed within a few repetitions. This initial familiarization can then serve as the prerequisite to the integration of novel items into existing knowledge. Future empirical and theoretical works can build on the current findings to develop a comprehensive model of the transition from novelty to familiarity.

Abrupt hippocampal remapping signals resolution of memory interference

Remapping refers to a decorrelation of hippocampal representations of similar spatial environments. While it has been speculated that remapping may contribute to the resolution of episodic memory interference in humans, direct evidence is surprisingly limited. We tested this idea using high-resolution, pattern-based fMRI analyses. Here we show that activity patterns in human CA3/dentate gyrus exhibit an abrupt, temporally-specific decorrelation of highly similar memory representations that is precisely coupled with behavioral expressions of successful learning. The magnitude of this learning-related decorrelation was predicted by the amount of pattern overlap during initial stages of learning, with greater initial overlap leading to stronger decorrelation. Finally, we show that remapped activity patterns carry relatively more information about learned episodic associations compared to competing associations, further validating the learning-related significance of remapping. Collectively, these findings establish a critical link between hippocampal remapping and episodic memory interference and provide insight into why remapping occurs.

Contextual prediction errors reorganize naturalistic episodic memories in time

Episodic memories are contextual experiences ordered in time. This is underpinned by associative binding between events within the same contexts. The role of prediction errors in declarative memory is well established but has not been investigated in the time dimension of complex episodic memories. Here we combine these two properties of episodic memory, extend them into the temporal domain and demonstrate that prediction errors in different naturalistic contexts lead to changes in the temporal ordering of event structures in them. The wrongly predicted older sequences were weakened despite their reactivation. Interestingly the newly encoded sequences with prediction errors, seen once, showed accuracy as high as control sequences which were viewed repeatedly without change. Drift-diffusion modelling revealed a lower decision threshold for the newer sequences than older sequences, reflected by their faster recall. Moreover, participants' adjustments to their decision threshold significantly correlated with their relative speed of sequence memory recall. These results suggest a temporally distinct and adaptive role for prediction errors in learning and reorganizing episodic temporal sequences.

Adaptive Repulsion of Long-Term Memory Representations Is Triggered by Event Similarity

We tested whether similarity between events triggers adaptive biases in how those events are remembered. We generated pairs of competing objects that were identical except in color and varied the degree of color similarity for the competing objects. Subjects (N = 123 across four experiments) repeatedly studied and were tested on associations between each of these objects and corresponding faces. As expected, high color similarity between competing objects created memory interference for object-face associations. Strikingly, high color similarity also resulted in a systematic bias in how the objects themselves were remembered: Competing objects with highly similar colors were remembered as being further apart (in color space) than they actually were. This repulsion of color memories increased with learning and served a clear adaptive purpose: Greater repulsion was associated with lower associative-memory interference. These findings reveal that similarity between events triggers adaptive-memory distortions that minimize interference.

Adaptive Memory Distortions Are Predicted by Feature Representations in Parietal Cortex

Similarity between memories is a primary cause of interference and forgetting. Exaggerating subtle differences between memories is therefore a potential mechanism for reducing interference. Here, we report a human fMRI study (n = 29, 19 female) that tested whether behavioral and neural expressions of memories are adaptively distorted to reduce interference. Participants learned and repeatedly retrieved object images, some of which were identical except for subtle color differences. Behavioral measures of color memory revealed exaggeration of differences between similar objects. Importantly, greater memory exaggeration was associated with lower memory interference. fMRI pattern analyses revealed that color information in parietal cortex was stronger during memory recall when color information was critical for discriminating competing memories. Moreover, greater representational distance between competing memories in parietal cortex predicted greater color memory exaggeration and lower memory interference. Together, these findings reveal that competition between memories induces adaptive, feature-specific distortions in parietal representations and corresponding behavioral expressions.SIGNIFICANCE STATEMENT Similarity between memories is a primary cause of interference and forgetting. Here, we show that, when remembering highly similar objects, subtle differences in the features of these objects are exaggerated in memory to reduce interference. These memory distortions are reflected in, and predicted by, overlap of activity patterns in lateral parietal cortex. These findings provide unique insight into how memory interference is resolved and specifically implicate lateral parietal cortex in representing feature-specific memory distortions.

Strategic retrieval prevents memory interference: The temporal dynamics of retrieval orientation

Resolving interference between overlapping memories is crucial to remember the past. This study tests the novel prediction that orienting search focus benefits goal-relevant retrieval by reducing competition from unwanted memories. In a modified retrieval-practice paradigm, participants encoded word-pairs in one of two encoding tasks. Critically, to evaluate whether this retrieval orientation (RO) reduces memory interference, target and competitor memories were always related to different encoding tasks. At retrieval, instructions were provided for half of the blocks with the intention to bias remembering towards items encoded with one of the ROs. Behavioural data show that adopting an RO improved target accessibility, strengthened the testing effect, and reduced retrieval-induced forgetting (RIF) of competitors. Specifically, RIF - typically attributed to inhibitory control of memory interference - was prominent when no retrieval orientation (NRO) instruction was provided. Furthermore, a neural correlate of RO was calculated by training a linear discriminant analysis (LDA) to discriminate the electroencephalographic (EEG) spatial brain patterns correspondent to the two ROs over the time course of selective retrieval. RO was characterised by increases in the theta and decreases in the beta frequency band, evident both before and after category-cue onset. While the pre-cue RO reinstatement effect predicted both immediate retrieval-practice success and later target accessibility, the post-cue effect predicted disengagement of inhibitory control, such that participants showing a stronger RO reinstatement effect showed lower levels of RIF. These data suggest that strategically orienting search focus during retrieval both increases target memory accessibility and reduces memory interference, which consequently protects related memories from inhibition and later forgetting. Furthermore, they also highlight the roles of theta and beta oscillations in establishing and maintaining a task-relevant bias towards target memory representations during competitive memory retrieval.

Memory Reactivation during Learning Simultaneously Promotes Dentate Gyrus/CA2,3 Pattern Differentiation and CA1 Memory Integration

Events that overlap with previous experience may trigger reactivation of existing memories. However, such reactivation may have different representational consequences within the hippocampal circuit. Computational theories of hippocampal function suggest that dentate gyrus and CA_2,3 (DG/CA_2,3) are biased to differentiate highly similar memories, whereas CA₁ may integrate related events by representing them with overlapping neural codes. Here, we tested whether the formation of differentiated or integrated representations in hippocampal subfields depends on the strength of memory reactivation during learning. Human participants of both sexes learned associations (AB pairs, either face-shape or scene-shape), and then underwent fMRI scanning while they encoded overlapping associations (BC shape-object pairs). Both before and after learning, participants were also scanned while viewing indirectly related elements of the overlapping memories (A and C images) in isolation. We used multivariate pattern analyses to measure reactivation of initial pair memories (A items) during overlapping pair (BC) learning, as well as learning-related representational change for indirectly related memory elements in hippocampal subfields. When prior memories were strongly reactivated during overlapping pair encoding, DG/CA_2,3 and subiculum representations for indirectly related images (A and C) became less similar, consistent with pattern differentiation. Simultaneously, memory reactivation during new learning promoted integration in CA₁, where representations for indirectly related memory elements became more similar after learning. Furthermore, memory reactivation and subiculum representation predicted faster and more accurate inference (AC) decisions. These data show that reactivation of related memories during new learning leads to dissociable coding strategies in hippocampal subfields, in line with computational theories.SIGNIFICANCE STATEMENT The flexibility of episodic memory allows us to remember both the details that differentiate similar events and the commonalities among them. Here, we tested how reactivation of past experience during new learning promotes formation of neural representations that might serve these two memory functions. We found that memory reactivation during learning promoted formation of differentiated representations for overlapping memories in the dentate gyrus/CA_2,3 and subiculum subfields of the hippocampus, while simultaneously leading to the formation of integrated representations of related events in subfield CA₁ Furthermore, memory reactivation and subiculum representation predicted success when inferring indirect relationships among events. These findings indicate that memory reactivation is an important learning signal that influences how overlapping events are represented within the hippocampal circuit.

Neural Overlap in Item Representations Across Episodes Impairs Context Memory

We frequently encounter the same item in different contexts, and when that happens, memories of earlier encounters can get reactivated. We examined how existing memories are changed as a result of such reactivation. We hypothesized that when an item's initial and subsequent neural representations overlap, this allows the initial item to become associated with novel contextual information, interfering with later retrieval of the initial context. Specifically, we predicted a negative relationship between representational similarity across repeated experiences of an item and subsequent source memory for the initial context. We tested this hypothesis in an fMRI study, in which objects were presented multiple times during different tasks. We measured the similarity of the neural patterns in lateral occipital cortex that were elicited by the first and second presentations of objects, and related this neural overlap score to subsequent source memory. Consistent with our hypothesis, greater item-specific pattern similarity was linked to worse source memory for the initial task. In contrast, greater reactivation of the initial context was associated with better source memory. Our findings suggest that the influence of novel experiences on an existing context memory depends on how reliably a shared component (i.e., item) is represented across these episodes.

Prior knowledge promotes hippocampal separation but cortical assimilation in the left inferior frontal gyrus

An adaptive memory system rarely learns information tabula rasa, but rather builds on prior knowledge to facilitate learning. How prior knowledge influences the neural representation of novel associations remains unknown. Here, participants associated pairs of faces in two conditions: a famous, highly familiar face with a novel face or two novel faces while undergoing fMRI. We examine multivoxel activity patterns corresponding to individual faces before and after learning. The activity patterns representing members of famous-novel pairs becomes separated in the hippocampus, that is, more distinct from one another through learning, in striking contrast to paired novel faces that become similar. In the left inferior frontal gyrus, however, prior knowledge leads to integration, and in a specific direction: the representation of the novel face becomes similar to that of the famous face after learning, suggesting assimilation of new into old memories. We propose that hippocampal separation might resolve interference between existing and newly learned information, allowing cortical assimilation. Thus, associative learning with versus without prior knowledge relies on radically different computations.

Prior knowledge strongly impacts new learning, but its influence on the neural representation of novel information is unknown. Here, the authors show multiple neural codes for learning: prior knowledge leads to integrated cortical representations, while promoting hippocampal separation.

The prevalence and importance of statistical learning in human cognition and behavior

Statistical learning, the ability to extract regularities from the environment over time, has become a topic of burgeoning interest. Its influence on behavior, spanning infancy to adulthood, has been demonstrated across a range of tasks, both those labeled as tests of statistical learning and those from other learning domains that predated statistical learning research or that are not typically considered in the context of that literature. Given this pervasive role in human cognition, statistical learning has the potential to reconcile seemingly distinct learning phenomena and may be an under-appreciated but important contributor to a wide range of human behaviors that are studied as unrelated processes, such as episodic memory and spatial navigation.

BrainIAK tutorials: User-friendly learning materials for advanced fMRI analysis

Advanced brain imaging analysis methods, including multivariate pattern analysis (MVPA), functional connectivity, and functional alignment, have become powerful tools in cognitive neuroscience over the past decade. These tools are implemented in custom code and separate packages, often requiring different software and language proficiencies. Although usable by expert researchers, novice users face a steep learning curve. These difficulties stem from the use of new programming languages (e.g., Python), learning how to apply machine-learning methods to high-dimensional fMRI data, and minimal documentation and training materials. Furthermore, most standard fMRI analysis packages (e.g., AFNI, FSL, SPM) focus on preprocessing and univariate analyses, leaving a gap in how to integrate with advanced tools. To address these needs, we developed BrainIAK (brainiak.org), an open-source Python software package that seamlessly integrates several cutting-edge, computationally efficient techniques with other Python packages (e.g., Nilearn, Scikit-learn) for file handling, visualization, and machine learning. To disseminate these powerful tools, we developed user-friendly tutorials (in Jupyter format; https://brainiak.org/tutorials/) for learning BrainIAK and advanced fMRI analysis in Python more generally. These materials cover techniques including: MVPA (pattern classification and representational similarity analysis); parallelized searchlight analysis; background connectivity; full correlation matrix analysis; inter-subject correlation; inter-subject functional connectivity; shared response modeling; event segmentation using hidden Markov models; and real-time fMRI. For long-running jobs or large memory needs we provide detailed guidance on high-performance computing clusters. These notebooks were successfully tested at multiple sites, including as problem sets for courses at Yale and Princeton universities and at various workshops and hackathons. These materials are freely shared, with the hope that they become part of a pool of open-source software and educational materials for large-scale, reproducible fMRI analysis and accelerated discovery.

The analysis of brain activity, as measured using functional magnetic resonance imaging (fMRI), has led to significant discoveries about how the brain processes information and how this is affected by disease. However, exhaustive multivariate analyses in space and time, run across a large number of subjects, can be complex and computationally intensive, creating a high barrier for entry into this field. Furthermore, the materials available to learn these methods do not encompass all the methods used, work is often published with no publicly available code, and the analyses are often difficult to run on large datasets without cluster computing. We have created interactive software tutorials that make it easy to understand and execute advanced analyses on fMRI data using the BrainIAK package—an open-source package built in Python. We have released these tutorials freely to the public and have significantly reduced computational roadblocks for users by making it possible to run the tutorials with a web browser and internet connection. We hope that this facilitated access and the usability of the underlying code—a compendium for how to program and optimize the latest fMRI analyses—will accelerate training, reproducibility, and discovery in cognitive neuroscience.

Predictability Changes What We Remember in Familiar Temporal Contexts

The human brain constantly anticipates the future based on memories of the past. Encountering a familiar situation reactivates memory of previous encounters, which can trigger a prediction of what comes next to facilitate responsiveness. However, a prediction error can lead to pruning of the offending memory, a process that weakens its representation in the brain and leads to forgetting. Our goal in this study was to evaluate whether memories are spared from such pruning in situations that allow for accurate predictions at the categorical level, despite prediction errors at the item level. Participants viewed a sequence of objects, some of which reappeared multiple times ("cues"), followed always by novel items. Half of the cues were followed by new items from different (unpredictable) categories, while others were followed by new items from a single (predictable) category. Pattern classification of fMRI data was used to identify category-specific predictions after each cue. Pruning was observed only in unpredictable contexts, while encoding of new items was less robust in predictable contexts. These findings demonstrate that how associative memories are updated is influenced by the reliability of abstract-level predictions in familiar contexts.

Interference between overlapping memories is predicted by neural states during learning

One of the primary contributors to forgetting is interference from overlapping memories. Intuitively, this suggests-and prominent theoretical models argue-that memory interference is best avoided by encoding overlapping memories as if they were unrelated. It is therefore surprising that reactivation of older memories during new encoding has been associated with reduced memory interference. Critically, however, prior studies have not directly established why reactivation reduces interference. Here, we first developed a behavioral paradigm that isolates the negative influence that overlapping memories exert during memory retrieval. We then show that reactivating older memories during the encoding of new memories dramatically reduces this interference cost at retrieval. Finally, leveraging multiple fMRI decoding approaches, we show that spontaneous reactivation of older memories during new encoding leads to integration of overlapping memories and, critically, that integration during encoding specifically reduces interference between overlapping, and otherwise competing, memories during retrieval.

Neural Representation of Overlapping Path Segments and Reward Acquisitions in the Monkey Hippocampus

Disambiguation of overlapping events is thought to be the hallmark of episodic memory. Recent rodent studies have reported that when navigating overlapping path segments in the different routes place cell activity in the same overlapping path segments were remapped according to different goal locations in different routes. However, it is unknown how hippocampal neurons disambiguate reward delivery in overlapping path segments in different routes. In the present study, we recorded monkey hippocampal neurons during performance of three virtual navigation (VN) tasks in which a monkey alternately navigated two different routes that included overlapping path segments (common central hallway) and acquired rewards in the same locations in overlapping path segments by manipulating a joystick. The results indicated that out of 106 hippocampal neurons, 57 displayed place-related activity (place-related neurons), and 18 neurons showed route-dependent activity in the overlapping path segments, consistent with a hippocampal role in the disambiguation of overlapping path segments. Moreover, 75 neurons showed neural correlates to reward delivery (reward-related neurons), whereas 56 of these 75 reward-related neurons showed route-dependent reward-related activity in the overlapping path segments. The ensemble activity of reward-related neurons represented reward delivery, locations, and routes in the overlapping path segments. In addition, ensemble activity patterns of hippocampal neurons more distinctly represented overlapping path segments than non-overlapping path segments. The present results provide neurophysiological evidence of disambiguation in the monkey hippocampus, consistent with a hippocampal role in episodic memory, and support a recent computational model of "neural differentiation," in which overlapping items are better represented by repeated retrieval with competitive learning.

Nonmonotonic Plasticity: How Memory Retrieval Drives Learning

What are the principles that govern whether neural representations move apart (differentiate) or together (integrate) as a function of learning? According to supervised learning models that are trained to predict outcomes in the world, integration should occur when two stimuli predict the same outcome. Numerous findings support this, but - paradoxically - some recent fMRI studies have found that pairing different stimuli with the same associate causes differentiation, not integration. To explain these and related findings, we argue that supervised learning needs to be supplemented with unsupervised learning that is driven by spreading activation in a U-shaped way, such that inactive memories are not modified, moderate activation of memories causes weakening (leading to differentiation), and higher activation causes strengthening (leading to integration).

Retrieval of Emotional Events from Memory

The enhancing effects of emotion on memory have been well documented; emotional events are often more frequently and more vividly remembered than their neutral counterparts. Much of the prior research has emphasized the effects of emotion on encoding processes and the downstream effects of these changes at the time of retrieval. In the current review, we focus specifically on how emotional valence influences retrieval processes, examining how emotion influences the experience of remembering an event at the time of retrieval (retrieval as an end point) as well as how emotion alters the way in which remembering the event affects the underlying memory representation and subsequent retrievals (retrieval as a starting point). We suggest ways in which emotion may augment or interfere with the selective enhancement of particular memory details, using both online and offline processes, and discuss how these effects of emotion may contribute to memory distortions in affective disorders.

More Is Less: Increased Processing of Unwanted Memories Facilitates Forgetting

The intention to forget can produce long-lasting effects. This ability has been linked to suppression of both rehearsal and retrieval of unwanted memories, processes mediated by the prefrontal cortex and hippocampus. Here, we describe an alternative account in which the intention to forget is associated with increased engagement with the unwanted information. We used pattern classifiers to decode human functional magnetic resonance imaging data from a task in which male and female participants viewed a series of pictures and were instructed to remember or forget each one. Pictures followed by a forget instruction elicited higher levels of processing in the ventral temporal cortex compared with those followed by a remember instruction. This boost in processing led to more forgetting, particularly for items that showed moderate (vs weak or strong) activation. This result is consistent with the nonmonotonic plasticity hypothesis, which predicts weakening and forgetting of memories that are moderately activated.SIGNIFICANCE STATEMENT The human brain cannot remember everything. Forgetting has a critical role in curating memories and discarding unwanted information. Intentional forgetting has traditionally been linked to passive processes, such as the withdrawal of sustained attention or a stoppage of memory rehearsal. It has also been linked to active suppression of memory processes during encoding and retrieval. Using functional magnetic resonance imaging and machine-learning methods, we show new evidence that intentional forgetting involves an enhancement of memory processing in the sensory cortex to achieve desired forgetting of recent visual experiences. This enhancement temporarily boosts the activation of the memory representation and renders it vulnerable to disruption via homeostatic regulation. Contrary to intuition, deliberate forgetting may involve more rather than less attention to unwanted information.

Reductions in Retrieval Competition Predict the Benefit of Repeated Testing

Repeated testing leads to improved long-term memory retention compared to repeated study, but the mechanism underlying this improvement remains controversial. In this work, we test the hypothesis that retrieval practice benefits subsequent recall by reducing competition from related memories. This hypothesis implies that the degree of reduction in competition between retrieval practice attempts should predict subsequent memory for practiced items. To test this prediction, we collected electroencephalography (EEG) data across two sessions. In the first session, participants practiced selectively retrieving exemplars from superordinate semantic categories (high competition), as well as retrieving the names of the superordinate categories from exemplars (low competition). In the second session, participants repeatedly studied and were tested on Swahili-English vocabulary. One week after session two, participants were again tested on the vocabulary. We trained a within-subject classifier on the data from session one to distinguish high and low competition states. We then used this classifier to measure the change in competition across multiple successful retrieval practice attempts in the second session. The degree to which competition decreased for a given vocabulary word predicted whether it was subsequently remembered in the third session. These results are consistent with the hypothesis that repeated testing improves retention by reducing competition.

Surprise and destabilize: prediction error influences episodic memory reconsolidation

Through the process of "reconsolidation," reminders can temporarily destabilize memories and render them vulnerable to change. Recent rodent research has proposed that prediction error, or the element of surprise, is a key component of this process; yet, this hypothesis has never before been extended to complex episodic memories in humans. In our novel paradigm, we used naturalistic stimuli to demonstrate that prediction error enables adaptive updating of episodic memories. In Study 1, participants (N = 48) viewed 18 videos, each depicting an action-outcome event. The next day, we reactivated these memories by presenting the videos again. We found that incomplete reminders, which interrupted videos before the outcome, made memories vulnerable to subsequent interference from a new set of videos, producing false memories. In Study 2 (N = 408), an independent sample rated qualities of the stimuli. We found that videos that were more surprising when interrupted produced more false memories. Last, in Study 3 (N = 24), we tested competing predictions of reconsolidation theory and the Temporal Context Model, an alternative account of source confusion. Consistent with the mechanistic time-course of reconsolidation, our effects were crucially time-dependent. Overall, we synthesize prior animal and human research to present compelling evidence that prediction error destabilizes episodic memories and drives dynamic updating in the face of new information.