Biyu Jade He

This study investigates the sequence of neural processing in conscious object recognition using EEG, finding that basic-level category information correlates with awareness early, while superordinate information becomes relevant later.

The paper compares human and machine vision by testing DNNs on systematically perturbed images, finding persistent differences in cue reliance and performance.

This study identifies high-level visual cortex as the neural substrate for one-shot perceptual learning and develops a deep neural network model that replicates human behavior and neural coding.

The paper tests subjective inflation under inattention, finding robust inflation at and above visual thresholds but not for specific features at suprathreshold levels.

This study investigates the spatiotemporal neural dynamics of object recognition under uncertainty using MEG and fMRI in humans, finding distinct patterns in ventral visual and frontoparietal regions.

The paper identifies frequency-specific neural signatures in MEG data that distinguish perceptual content from perceptual stability during bistable perception.

The paper shows that task states reduce neural correlations and variability across cortical areas in both primate and human data, increasing information coding capacity.

The paper improves sensory input detection in awake mice by using a cortical state classifier to account for trial-to-trial variability in neural responses.

The paper uses MEG to show that initial brain states predict conscious visual perception, with distinct activity trajectories for seen and unseen stimuli.