Yaoda Xu A

Increased Cognitive Load Expands, Stabilizes, and Aligns Visual Representation Across the Human Brain

This study shows that increased cognitive load expands and stabilizes visual representations across the brain, enhances cross-regional coordination, and reduces neural variability.

Neural Representations of Ensemble Mean and Variance Across Visual Features

This study investigates how the brain represents ensemble mean and variance across different visual features using fMRI and multivariate pattern analysis, finding distinct neural pathways for mean and variance processing.

Representing Multiple Visual Objects in the Human Brain and Convolutional Neural Networks

This paper compares averaging mechanisms in human brain regions and CNNs for object processing, finding that averaging occurs at both single-unit and population levels in the brain but not in CNNs.

Understanding transformation tolerant visual object representations in the human brain and convolutional neural networks

This paper compares transformation tolerance in human brain and CNNs, finding that the brain develops tolerance across feature changes while CNNs do not.

Representation of Color, Form, and their Conjunctionacross the Human Ventral Visual Pathway

This study uses fMRI to examine how color and form are coded in the human ventral visual pathway, finding biased distributed representation and relative independence between color and form coding.

Predicting identity-preserving object transformations across the human ventral visual stream

The paper investigates how object identity and non-identity features are represented in the human ventral visual pathway using linear mapping functions across different transformations.

Limited correspondence in visual representation between the human brain and convolutional neural networks

The paper compares fMRI responses from the human brain with 14 CNNs, finding partial correspondence in lower layers but not in higher layers or artificial objects.

The relative coding strength of object identity and nonidentity features in human occipito-temporal cortex and convolutional neural networks

The paper compares the coding strength of object identity and nonidentity features in human visual cortex and CNNs, finding increased identity representation and decreased nonidentity representation along the ventral visual pathway.

Joint Representation of Color and Shape in Convolutional Neural Networks: A Stimulus-rich Network Perspective

This paper examines how convolutional neural networks code color and shape features, finding stronger interaction in trained networks compared to untrained ones.