Marvin M. Chun

This study shows that increased cognitive load expands and stabilizes visual representations across the brain, enhances cross-regional coordination, and reduces neural variability.

The paper investigates how the brain updates social impressions during movie watching, finding that neural synchrony and insight moments drive impression changes.

This study uses connectome-based predictive modeling to predict individual differences in three executive function components using fMRI data from the Human Connectome Project.

This study shows that ongoing thoughts during rest are reflected in brain connectivity and predict individual behavioral differences.

The paper investigates attentional blink as a selective control mechanism in conscious perception using behavioral and neurophysiological measures.

The paper examines brain-behavior relationships using fMRI data and finds that neural similarity does not always correlate with behavioral similarity across different domains.

The paper uses edge-based general linear models to analyze fMRI data and identifies high-frequency network fluctuations related to attention.

The paper shows that functional brain networks predict sustained attention and working memory in children and adults, with WM networks changing more from preadolescence to adulthood.

This paper compares averaging mechanisms in human brain regions and CNNs for object processing, finding that averaging occurs at both single-unit and population levels in the brain but not in CNNs.

This paper introduces a connectome-to-connectome state transformation model to quantify brain reorganization across cognitive states using fMRI data.

The paper develops a universal attention measure from fMRI data that predicts both task-general and task-specific attention performance and their neural mechanisms across multiple tasks.

The paper investigates how object identity and non-identity features are represented in the human ventral visual pathway using linear mapping functions across different transformations.

This paper introduces a functional searchlight method to analyze fMRI data, revealing that visual, auditory, and semantic features are more distributed in the brain than previously thought.

The paper investigates whether word embeddings align with brain representations and improves them using brain features, showing increased coherence with human semantic judgments.

This study develops a whole-brain functional connectivity model using fMRI data to predict individual differences in long-term memory, showing it is distinct from working memory networks.

This study shows that a validated functional connectivity model predicts attention changes across time and under different sedation states.

The paper demonstrates zero-shot decoding of novel visual categories using neural responses from macaque inferior temporal cortex mapped onto a deep neural network.

This study uses connectome-based predictive modeling on fMRI data to identify functional brain networks that predict social ability measures in autism spectrum disorder.

This study predicts eye movements from spatial priority maps reconstructed from fMRI data using a deep CNN model pre-trained for scene categorization.