The regulatory mechanisms of multi-channel category learning in feature processing and category selection

This study combines the DDM model with event-related potential techniques to investigate the effects of multi-channel category learning on feature processing and category selection. The results show that at the level of feature processing, prototype stimuli lead to higher accuracy and shorter response times compared to variable stimuli. Neural representations indicate an increase in N250 amplitudes, Frontal Selection Positivity (FSP), and Late Positive Component amplitudes, as well as an increase in Alpha and Beta energy levels.