Jack L. Gallant

The paper investigates how attention modulates visual-semantic representations in the cerebellum using fMRI and encoding models, finding significant tuning shifts in specific cerebellar regions.

The paper generates video clips that maximize activity in cortical navigation regions, revealing dynamic scenes that reflect functional properties of these regions.

The paper investigates how visual semantic tuning in the human brain shifts between two naturalistic tasks, movie watching and navigation, showing task-dependent changes in object category representation.

This study investigates how semantic information is represented in the brains of bilinguals, finding shared representations between languages with some language-specific differences.

This study investigates how semantic relations and concepts are represented in the human cerebral cortex using fMRI, finding that relations are distinct from their relata and share similar cortical regions with concepts.

The paper maps a cortical network of 11 functionally distinct regions involved in naturalistic navigation using fMRI and banded ridge regression.

This paper develops a statistical framework to measure individual differences in conceptual brain representations using fMRI and finds that these differences are systematic and highest in social information processing regions.

The paper introduces a sparse concept encoding model to improve interpretability of brain encoding models by disentangling semantic features in fMRI data.

The paper develops a hierarchical convolutional energy model to predict V4 neuron responses to natural videos and finds it captures complex spatiotemporal selectivity in V4.

The paper examines whether language timescale representations in the brain are shared between reading and listening using fMRI data.

This paper introduces model connectivity as a method to recover functional brain networks by analyzing encoding model weights, showing it outperforms functional connectivity in noise robustness and interpretability.

This study shows that context significantly affects the quality of neuroimaging data and the representation of semantic information in the brain during language comprehension.

This paper introduces banded ridge regression for feature-space selection in brain recordings, improving prediction accuracy and interpretability by adapting regularization per feature space.

The paper investigates how attention modulates semantic representations during visual search for action categories, finding widespread tuning shifts across neocortex.

This paper introduces Tikhonov regression for fMRI data modeling, showing it can improve predictive models by using non-spherical Gaussian priors instead of spherical ridge regression.

The paper introduces DeepTune, a framework to interpret neural network models of V4 neurons, revealing diverse shape/texture tuning and suppressive responses in primate visual cortex.