S. Nishida

This study investigates whether symmetric semantic information is represented symmetrically in the human brain using fMRI data from movie-evoked responses.

This study shows that human attractiveness ratings of AI-synthesized faces decrease due to belief in their synthetic origin, not their appearance, and this effect is reflected in neural responses in the fusiform cortex.

The paper investigates the neural correlates of the phenomenological attitude (PA) by comparing brain activity during PA and natural attitude (NA) using fMRI, finding distinct activation patterns in action-related brain regions.

This paper introduces a framework using CNNs to decode individual mental information from predicted brain responses, achieving high accuracy without requiring additional fMRI measurements.

The paper compares semantic dissimilarities of words derived from brain models and human behavior, finding a significant correlation that supports the validity of word vector-based semantic representations in the brain.

The paper identifies brain regions associated with programming expertise by decoding source code categories from brain activity and correlating decoding accuracy with behavioral performance.

This study examines semantic representations in schizophrenia patients using fMRI and finds reduced intra- and inter-individual variability compared to healthy controls.