Yu Zhang

The paper investigates how statistical learning drives distractor suppression through dynamic attentional states modulated by threat, showing threat accelerates suppression transitions.

This study uses fNIRS to examine neural dynamics during aesthetic poetry reading, revealing a three-stage pattern involving semantic processing, inhibition, and reactivation linked to aesthetic appreciation.

This study shows that increased student-teacher inter-brain coupling predicts academic improvement in real-world classrooms using longitudinal EEG data.

The paper examines how inter-brain coupling differs between soft and hard disciplines in classroom learning, finding distinct coupling patterns associated with learning outcomes.

This study examines how neurophysiological data can represent situational interest in classroom learning by comparing laboratory and naturalistic paradigms using machine learning and structural equation modeling.

This study shows that physiological synchrony, measured via EDA and HR, can predict collaboration quality in classroom settings based on dyad interaction patterns.