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

Ahmad T. Qamar, R. James Cotton, Ryan G. George, Jeffrey M. Beck, Eugenia Prezhdo, Allison Laudano, Andreas S. Tolias, Wei Ji Ma

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)20332-20337
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume110
Issue number50
DOIs
StatePublished - Dec 10 2013

Keywords

  • Bayesian inference
  • Decision-making
  • Optimality
  • Vision

ASJC Scopus subject areas

  • General

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