Kenneth A. Norman

Reactivation during sleep segregates the neural representations of episodic memories

The paper shows that reactivation during sleep reduces overlap between neural representations of contextually linked memories, promoting object and context specificity.

Binding items to contexts through conjunctive neural representations with the Method of Loci

The paper investigates how the brain forms connections between new information and existing schemas using the Method of Loci, finding that default mode network regions create conjunctive representations that evolve with expertise.

A neural mechanism for online discovery of latent contexts

This paper introduces NeuraGEM, a neural architecture that models online discovery of latent contexts through fast transient activity and slow synaptic plasticity, reproducing human contextual learning features and brain-like dynamics.

Synthesizing images to map neural networks to the human brain

The paper develops a method to synthesize images for mapping neural network layers to brain regions using fMRI, finding more selective mappings than natural images.

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

The paper investigates how music-evoked reactivation in the default mode network during film viewing relates to improved recall of naturalistic events the next day.

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

The paper investigates how context-dependent temporal structures are represented in the brain during narrative processing and finds that these representations predict memory for details.

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

The paper investigates neural state patterns during memory search, showing hippocampal-neocortical communication facilitates retrieval of event-related information.

Sculpting New Visual Categories into the Human Brain

This paper demonstrates that neural activity patterns in the human brain can be directly modified to create new visual categories, leading to behavioral and neural biases for the sculpted categories.

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

The paper shows that reliable neural representations of spatial contexts predict better memory for associated objects.

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

This study investigates whether REM sleep facilitates hippocampal memory differentiation after prediction errors, finding greater differentiation in REM sleep conditions in specific hippocampal regions.

Inducing representational change in the hippocampus through real-time neurofeedback

The paper uses real-time fMRI neurofeedback to induce coactivation of competing memories in visual cortex, leading to behavioral and neural integration in the hippocampus.

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

This paper analyzes the functional specialization of transformer-based language models and finds similarities with the human brain's cortical language network.

Episodic memory supports the acquisition of structured task representations

This study shows that episodic memory stabilizes task representations, enabling structured knowledge accumulation in a neural network model.

Test-Retest Reliability of the Human Connectome: An OPM-MEG study

This study evaluates the test-retest reliability of OPM-MEG for whole-brain functional connectivity, showing high group-level repeatability and individual robustness across theta, alpha, and beta frequency ranges.

Differentiation and Integration of Competing Memories: A Neural Network Model

This paper introduces an unsupervised neural network model that explains memory integration and differentiation based on competitor activity levels, showing rapid and asymmetric differentiation with anticorrelated representations.

Associative memory retrieval modulates upcoming perceptual decisions.

The paper shows that perceptual decisions are influenced by dynamic memory retrieval and sensory evidence, with memory parameters adjusting based on trial-specific evidence.

Here's the twist: How the brain updates the representations of naturalistic events as our understanding of the past changes

The paper investigates how the brain updates memory representations of naturalistic events when new information changes understanding of the past, showing dynamic neural changes in the default mode network.

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

This paper presents a neural network model simulating hippocampus-neocortex interactions during sleep to explain memory consolidation and continual learning.

Real-time neurofeedback to alter interpretations of a naturalistic narrative

This study uses real-time fMRI neurofeedback to influence interpretations of a naturalistic narrative, finding that higher decoding accuracy correlates with successful interpretation shifts.

Schema representations in distinct brain networks support narrative memory during encoding and retrieval

When to retrieve and encode episodic memories: a neural network model of hippocampal-cortical interaction

This study uses a neural network model to show that selective retrieval and encoding of episodic memories improves prediction performance by reducing the risk of irrelevant memory retrieval.

High-order areas and auditory cortex both represent the high-level event structure of music

The paper uses fMRI and hidden Markov models to show that brain regions involved in narrative event segmentation also represent high-level event structure in music.

Increasing stimulus similarity drives nonmonotonic representational change in hippocampus

The paper shows that hippocampal representational change follows a U-shaped function of stimulus similarity during learning.

Brain kernel: a new spatial covariance function for fMRI data

The paper introduces a new spatial covariance function for fMRI data called the brain kernel, which captures non-stationary spatial correlations and improves decoding and factor analysis.

Moment-by-moment tracking of naturalistic learning and its underlying hippocampo-cortical interactions

The paper tracks memory formation and retrieval during story listening, showing neural correlates of predictive recall and hippocampo-cortical interactions.

Think Like an Expert: Neural Alignment Predicts Understanding in Students Taking an Introduction to Computer Science Course

This study introduces a neural measure called neural alignment to predict learning outcomes by comparing students' brain activity to experts and peers in a computer science course.

Relating the past with the present: Information integration and segregation during ongoing narrative processing

This study investigates how the brain integrates and segregates information during narrative processing, showing that neural patterns specific to storylines are reinstated during transitions and that motif reinstatement correlates with better integration of events.

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

The paper shows that multiple memories can be reactivated during sleep without reducing their consolidation benefits, suggesting parallel memory processing during sleep.

Narratives: fMRI data for evaluating models of naturalistic language comprehension

The paper introduces a benchmark dataset of fMRI data from human subjects listening to naturalistic stories for evaluating language and narrative comprehension models.

Thinking ahead: prediction in context as a keystone of language in humans and machines

In nine epilepsy patients, cortical activity carries next-word information up to 800 ms before onset, tracks GPT-2 surprise after onset, and fits GPT-2 contextual embeddings better than static ones.