title: Behavior and neural basis of near-optimal visual search.
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.
authors.affiliation: Department of Neuroscience, Baylor College of Medicine, Houston, Texas, USA. wjma@bcm.edu
authors.forename: Wei Ji
authors.initials: WJ
authors.lastname: Ma
authors.forename: Vidhya
authors.initials: V
authors.lastname: Navalpakkam
authors.forename: Jeffrey M
authors.initials: JM
authors.lastname: Beck
authors.forename: Ronald van den
authors.initials: Rv
authors.lastname: Berg
authors.forename: Alexandre
authors.initials: A
authors.lastname: Pouget
mesh_terms: D004192:Discrimination, Psychological; D006801:Humans; D008959:Models, Neurological; D019370:Observation / Q000379:methods; D010775:Photic Stimulation / Q000379:methods; D015203:Reproducibility of Results; D014796:Visual Perception