Trial-to-trial, uncertainty-based adjustment of decision boundaries in visual categorization

Journal Article (Journal Article)

Categorization is a cornerstone of perception and cognition. Computationally, categorization amounts to applying decision boundaries in the space of stimulus features. We designed a visual categorization task in which optimal performance requires observers to incorporate trial-to-trial knowledge of the level of sensory uncertainty when setting their decision boundaries. We found that humans and monkeys did adjust their decision boundaries from trial to trial as the level of sensory noise varied, with some subjects performing near optimally. We constructed a neural network that implements uncertainty-based, near-optimal adjustment of decision boundaries. Divisive normalization emerges automatically as a key neural operation in this network. Our results offer an integrated computational and mechanistic framework for categorization under uncertainty.

Full Text

Duke Authors

Cited Authors

  • Qamar, AT; Cotton, RJ; George, RG; Beck, JM; Prezhdo, E; Laudano, A; Tolias, AS; Ma, WJ

Published Date

  • December 10, 2013

Published In

Volume / Issue

  • 110 / 50

Start / End Page

  • 20332 - 20337

Electronic International Standard Serial Number (EISSN)

  • 1091-6490

International Standard Serial Number (ISSN)

  • 0027-8424

Digital Object Identifier (DOI)

  • 10.1073/pnas.1219756110

Citation Source

  • Scopus