Cost-sensitive Bayesian control policy in human active sensing.

ABSTRACT: An important but poorly understood aspect of sensory processing is the role of active sensing, the use of self-motion such as eye or head movements to focus sensing resources on the most rewarding or informative aspects of the sensory environment. Here, we present behavioral data from a visual search experiment, as well as a Bayesian model of within-trial dynamics of sensory processing and eye movements. Within this Bayes-optimal inference and control framework, which we call C-DAC (Context-Dependent Active Controller), various types of behavioral costs, such as temporal delay, response error, and sensor repositioning cost, are explicitly minimized. This contrasts with previously proposed algorithms that optimize abstract statistical objectives such as anticipated information gain (Infomax

SUBMITTER: Ahmad S 

PROVIDER: S-EPMC4253738 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature