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Proc Natl Acad Sci U S A. 2013 Dec 10;110(50):20332-7. doi: 10.1073/pnas.1219756110. Epub 2013 Nov 22.

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

Author information

1
Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030.

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.

KEYWORDS:

Bayesian inference; decision-making; optimality; vision

PMID:
24272938
PMCID:
PMC3864339
DOI:
10.1073/pnas.1219756110
[Indexed for MEDLINE]
Free PMC Article

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