动态视觉-动作控制中的信息整合机制OACHSSCD

Psychological research has long assumed that behavioral responses can be directly mapped onto underlying mental processes,and this assumption has guided operational definitions and experimental designs.In recent years,however,researchers have begun to reexamine this relationship,investigating how the traditional stimulus-response framework may systematically vary depending on the mode of response,and exploring the mechanisms underlying such variation.One emerging approach adopts a Bayesian framework to reconstruct the relationship among sensory input,internal processing,and behavioral output,thereby explaining how stimulus-response mappings may adapt to task characteristics.Building on this perspective,the present proposal focuses on the stimulus-response relationship as a central theoretical problem.We propose to employ a Kalman filter model within a Bayesian framework to analyze visual motion control.By examining behavioral tracking performance together with electrophysiological activity in visual cortical areas,the study aims to reverse-engineer the visuomotor control process and dissociate two key components:perceptual encoding and information integration.The respective influences of these components on visuomotor behavior will be tested,with particular emphasis on how the information-integration component modulates the mapping between stimuli and responses.The primary objective of this project is to evaluate the effectiveness of the Kalman filter model in explaining the relationship among information input,mental processing,and behavioral response,and to explore its potential applications in improving the design and interpretability of psychological experiments.