The Role of Rapid Eye Movement 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 cue from the mispredicted item itself, reducing the likelihood of future prediction errors. Prior work by Kim, Norman, and Turk-Browne (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.

Towards large language models with human-like episodic memory.

Cognitive neuroscience research has made tremendous progress over the past decade in addressing how episodic memory (EM; memory for unique past experiences) supports our ability to understand real-world events. Despite this progress, we still lack a computational modeling framework that is able to generate precise predictions regarding how EM will be used when processing high-dimensional naturalistic stimuli. Recent work in machine learning that augments large language models (LLMs) with external memory could potentially accomplish this, but current popular approaches are misaligned with human memory in various ways.

Music-evoked reactivation during continuous perception is associated with enhanced subsequent recall of naturalistic events.

Music is a potent cue for recalling personal experiences, yet the neural basis of music-evoked memory remains elusive. We address this question by using the full-length film Eternal Sunshine of the Spotless Mind to examine how repeated musical themes reactivate previously encoded events in cortex and shape next-day recall. Participants in an fMRI study viewed either the original film (with repeated musical themes) or a no-music version.

Fast-timescale hippocampal processes bridge between slowly unfurling neocortical states during memory search.

Prior behavioral work showed that event structure plays a key role in our ability to mentally search through memories of continuous naturalistic experience. We hypothesized that, neurally, this memory search process involves a division of labor between slowly unfurling neocortical states representing event knowledge and fast hippocampal-neocortical communication that supports retrieval of new information at transitions between events. To test this, we tracked slow neural state-patterns in a sample of ten patients undergoing intracranial electroencephalography as they viewed a movie and then searched their memories in a structured naturalistic interview.

Neural codes track prior events in a narrative and predict subsequent memory for details.

Throughout our lives, we learn schemas that specify what types of events to expect in particular contexts and the temporal order in which these events usually occur. Here, our first goal was to investigate how such context-dependent temporal structures are represented in the brain during processing of temporally extended events. To accomplish this, we ran a 2-day fMRI study (N = 40) in which we exposed participants to many unique animated videos of weddings composed of sequences of rituals; each sequence originated from one of two fictional cultures (North and South), where rituals were shared across cultures, but the transition structure between these rituals differed across cultures.

Large language models can segment narrative events similarly to humans.

Humans perceive discrete events such as "restaurant visits" and "train rides" in their continuous experience. One important prerequisite for studying human event perception is the ability of researchers to quantify when one event ends and another begins. Typically, this information is derived by aggregating behavioral annotations from several observers.

How sturdy is your memory palace? Reliable room representations predict subsequent reinstatement of placed objects.

Our autobiographical experiences typically occur within the context of familiar spatial locations. When we encode these experiences into memory, we can use our spatial map of the world to help organize these memories and later retrieve their episodic details. However, it is still not well understood what psychological and neural factors make spatial contexts an effective scaffold for storing and accessing memories.

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.

Study while you sleep: using targeted memory reactivation as an independent research project for undergraduates.

Newly acquired information is stabilized into long-term memory through the process of consolidation. Memories are not static; rather, they are constantly updated via reactivation, and this reactivation occurs preferentially during slow-wave sleep (SWS; also referred to as N3 in humans). Here we present a scalable neuroscience research investigation of memory reactivation using low-cost electroencephalogram (EEG) recording hardware and open-source software for students and educators across the K-12 and higher education spectrum.

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.

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.

Blocked training facilitates learning of multiple schemas.

We all possess a mental library of schemas that specify how different types of events unfold. How are these schemas acquired? A key challenge is that learning a new schema can catastrophically interfere with old knowledge. One solution to this dilemma is to use interleaved training to learn a single representation that accommodates all schemas.

Shared functional specialization in transformer-based language models and the human brain.

When processing language, the brain is thought to deploy specialized computations to construct meaning from complex linguistic structures. Recently, artificial neural networks based on the Transformer architecture have revolutionized the field of natural language processing. Transformers integrate contextual information across words via structured circuit computations.

Eye tracking evidence for the reinstatement of emotionally negative and neutral memories.

Recent eye tracking studies have linked gaze reinstatement-when eye movements from encoding are reinstated during retrieval-with memory performance. In this study, we investigated whether gaze reinstatement is influenced by the affective salience of information stored in memory, using an adaptation of the emotion-induced memory trade-off paradigm. Participants learned word-scene pairs, where scenes were composed of negative or neutral objects located on the left or right side of neutral backgrounds.

Improving Memory Search Through Model-Based Cue Selection.

We often use cues from our environment when we get stuck searching our memories, but prior research has failed to show benefits of cuing with other, randomly selected list items during memory search. What accounts for this discrepancy? We proposed that cues' content critically determines their effectiveness and sought to select the right cues by building a computational model of how cues affect memory search. Participants ( = 195 young adults from the United States) recalled significantly more items when receiving our model's best (vs.

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.

When working memory may be just working, not memory.

The -back task is often considered to be a canonical example of a task that relies on working memory (WM), requiring both maintenance of representations of previously presented stimuli and also processing of these representations. In particular, the set-size effect in this task (e.g.

Bayesian Surprise Predicts Human Event Segmentation in Story Listening.

Event segmentation theory posits that people segment continuous experience into discrete events and that event boundaries occur when there are large transient increases in prediction error. Here, we set out to test this theory in the context of story listening, by using a deep learning language model (GPT-2) to compute the predicted probability distribution of the next word, at each point in the story. For three stories, we used the probability distributions generated by GPT-2 to compute the time series of prediction error.

Test-retest reliability of the human connectome: An OPM-MEG study.

Magnetoencephalography with optically pumped magnetometers (OPM-MEG) offers a new way to record electrophysiological brain function, with significant advantages over conventional MEG, including adaptability to head shape/size, free movement during scanning, increased signal amplitude, and no reliance on cryogenics. However, OPM-MEG remains in its infancy, with significant questions to be answered regarding the optimal system design. Here, we present an open-source dataset acquired using a newly constructed OPM-MEG system with a triaxial sensor design, 168 channels, OPM-optimised magnetic shielding, and active background field control.

Optimal metacognitive control of memory recall.

Most of us have experienced moments when we could not recall some piece of information but felt that it was just out of reach. Research in metamemory has established that such judgments are often accurate; but what adaptive purpose do they serve? Here, we present an optimal model of how metacognitive monitoring (feeling of knowing) could dynamically inform metacognitive control of memory (the direction of retrieval efforts). In two experiments, we find that, consistent with the optimal model, people report having a stronger memory for targets they are likely to recall and direct their search efforts accordingly, cutting off the search when it is unlikely to succeed and prioritizing the search for stronger memories.

Associative memory retrieval modulates upcoming perceptual decisions.

Expectations can inform fast, accurate decisions. But what informs expectations? Here we test the hypothesis that expectations are set by dynamic inference from memory. Participants performed a cue-guided perceptual decision task with independently-varying memory and sensory evidence.

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.

Large language models can segment narrative events similarly to humans.

Humans perceive discrete events such as "restaurant visits" and "train rides" in their continuous experience. One important prerequisite for studying human event perception is the ability of researchers to quantify when one event ends and another begins. Typically, this information is derived by aggregating behavioral annotations from several observers.