Bounded Optimal State Estimation and Control in Visual Search: Explaining Distractor Ratio Effects

Abstract

We demonstrate that an ideal observer model bounded by known limitations of the human visual system can explain empirical evidence concerning two effects of distractor ratios on visual search—effects that have previously been explained with salience-based models. The model makes optimal state estimations based on Bayesian estimates of stimuli localization and optimal control decisions of where to fixate in order to maximize task performance. Analysis of the model’s behavior under different task strategies and different constraints on the visual system reveal which aspects of the model are responsible for the effects: the distractor-ratio effects on number of fixations is a signature of optimal state estimation in the face of noisy spatial information, and the saccadic-bias effect is a signature of both optimal control and estimation under these same bounds.


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