Vy A. Vo

This paper introduces a framework to improve deep learning model interpretability in language processing by simulating brain responses and identifying temporal context effects in human cortex.

This study compares the neural representations of working memory and long-term memory in occipital and parietal cortex using fMRI and multivariate pattern analysis, finding shared and distinct coding formats.

This paper develops interpretable multi-timescale models to predict fMRI responses to natural speech, revealing how different brain regions encode information at varying temporal scales.

The paper shows that high-reward distractors enhance their neural representations in early visual cortex when previously selected as targets, suggesting value-driven attentional capture starts with sensory modulation.

The paper investigates how visual salience and behavioral relevance independently influence attentional priority maps in human cortex using fMRI data.

The paper investigates how attentional modulations of single receptive fields affect population-level spatial representations in visual cortex, finding that position shifts of receptive fields enhance discriminability and fidelity.