Christopher Richard Cox

The paper uses representational similarity learning to show that multidimensional semantic information is encoded across multiple frequencies in human ventral anterior temporal lobe.

The paper decodes animacy from ECoG data in the human anterior ventral temporal cortex, finding that semantic information is encoded by local populations and interacts with distributed cortical regions.

The paper uses comparative multivariate decoding to evaluate theories of semantic representation in the anterior temporal lobes and other brain regions, finding domain-general vector-space codes in these areas.

The paper uses representational similarity learning to decode a graded multidimensional semantic space in the human anterior temporal cortex from ECoG data.

The paper uses fMRI to investigate how default network and semantic network encode social concepts, finding the default network better represents self vs. other distinctions.

This study shows semantic information in human ventral anterior temporal cortex is dynamically distributed with stable and changing elements, supporting a hub-like role in semantic networks.

This paper improves whole-brain fMRI neural decoding using domain adaptation, achieving an 8.94% accuracy improvement by leveraging pre-existing source data.

This study introduces a structured sparsity method to analyze functional brain imaging data, revealing distributed signals missed by existing methods in visual face perception.