Kai Yin Hwang

Abstract Cognitive control organizes abstract contexts, stimuli, and actions into hierarchically structured representations. This organization supports flexible behavior but requires updating at multiple levels of the hierarchy, a process reflected in task switch costs. However, it remains unclear how updating differs across levels of abstraction and how these differences relate to behavior and neural representations.

Abstract Adaptive behavior requires integrating multiple sources of information, yet neural and computational mechanisms underlying this process are poorly understood. We introduce a novel Bayesian computational framework elucidating how internally maintained and externally cued information are integrated to guide goal-directed actions. Our model infers trial-by-trial beliefs for inputs and integrates them into a joint probability distribution, where entropy quantifies overall uncertainty.