Active Fovea-Based Vision Through Computationally-Effective Model-Based Prediction.

ABSTRACT: What motivates an action in the absence of a definite reward? Taking the case of visuomotor control, we consider a minimal control problem that is how select the next saccade, in a sequence of discrete eye movements, when the final objective is to better interpret the current visual scene. The visual scene is modeled here as a partially-observed environment, with a generative model explaining how the visual data is shaped by action. This allows to interpret different action selection metrics proposed in the literature, including the Salience, the Infomax and the Variational Free Energy, under a single information theoretic construct, namely the view-based Information Gain. Pursuing this analytic track, two original action selection metrics named the Information Gain Lower Bound (IGLB) and

SUBMITTER: Dauce E 

PROVIDER: S-EPMC6302111 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature