Chaipat Chunharas

The paper shows that prefrontal cortex organizes visual evidence by representational abstraction, with distinct regions processing lower- and higher-level similarity during decision-making.

This study shows that symptom lesion network mapping (sLNM) results are biased toward the first principal component of the normative connectome, leading to spurious convergence and non-specific clinical predictions, which can be corrected by controlling for PC1.

The paper investigates whether visual information in the human brain is represented in an allocentric or retinocentric reference frame using EEG and head tilt manipulations, finding an intermediate representation.

The paper shows that during working memory, orientation representations in the visual hierarchy become more categorical compared to perception, with a gradual shift toward categorical formats in more anterior regions.

This paper introduces an explainable self-attention deep neural network to detect mild cognitive impairment using multi-input digital drawing tasks, achieving better classification performance than existing models.

This study shows that human visual cortex can represent both working memory contents and new sensory inputs simultaneously at the population level.