Ning Mei

The paper shows that orthogonal representational geometry in dACC supports hierarchical reasoning by encoding error and difficulty along separate dimensions.

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.

The paper re-analyzes fMRI data to compare neural representations of conscious and unconscious perceptual content using convolutional neural network models, finding that these models predict brain responses across different regions and participants.

This paper uses fMRI-DecNef to show unconscious modification of conceptual representations, affecting visual search behavior without conscious awareness of the target content.

PyDecNef provides an open-source framework for real-time fMRI analysis, enabling accessible and customizable decoded neurofeedback experiments.

This study identifies neural representations of unconscious content in human brains and simulates it using deep networks, showing overlapping representations between conscious and unconscious processing.

This study shows that deeper mental simulation during word processing leads to more generalized and connected brain representations of meaning compared to shallow processing.

The paper shows that prospective decisions about future behavior share a common representational structure, as demonstrated by machine learning predictions across different tasks.

This study uses EEG and fMRI to show that a widespread cortical network supports both short- and long-term scene memory through distinct spatiotemporal neural dynamics.