Behavior and Neural Basis of Near-optimal Visual Search

Overview

Journal Nat Neurosci

Date 2011 May 10

PMID 21552276

Citations 44

Authors

Wei Ji Ma

Vidhya Navalpakkam

Jeffrey M Beck

Ronald van den Berg

Alexandre Pouget

Affiliations

Soon will be listed here.

Abstract

The ability to search efficiently for a target in a cluttered environment is one of the most remarkable functions of the nervous system. This task is difficult under natural circumstances, as the reliability of sensory information can vary greatly across space and time and is typically a priori unknown to the observer. In contrast, visual-search experiments commonly use stimuli of equal and known reliability. In a target detection task, we randomly assigned high or low reliability to each item on a trial-by-trial basis. An optimal observer would weight the observations by their trial-to-trial reliability and combine them using a specific nonlinear integration rule. We found that humans were near-optimal, regardless of whether distractors were homogeneous or heterogeneous and whether reliability was manipulated through contrast or shape. We present a neural-network implementation of near-optimal visual search based on probabilistic population coding. The network matched human performance.

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