Cited by 64Visual perception and processing mechanismsFace Recognition and PerceptionNeural and Behavioral Psychology Studies

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

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Qamar, Ahmad T., Cotton, R. James, George, Ryan G., Beck, Jeffrey M., Prezhdo, Eugenia, Laudano, Allison, Tolias, Andreas S., Ma, Wei Ji·2013·Proceedings of the National Academy of Sciences

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.

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Crossref · 10.1073/pnas.1219756110

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  • Correctness feedback was withheld during testing to avoid the possibility that confidence simply reflects a learned mapping between stimulus orientation and reliability and the probability of being correct.

    “During testing, correctness feedback was withheld to avoid the possibility that confidence simply reflects a learned mapping between stimulus orientation and reliability and the probability of being correct [30–33].”
  • The power law relationship encompasses a large family of monotonically decreasing relationships using only three parameters.

    “We have previously [30] used this power law relationship because it encompasses a large family of monotonically decreasing relationships using only three parameters.”

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Authors
Qamar, A. T., Cotton, R. J., George, R. G., Beck, J. M., Prezhdo, E., Laudano, A., Tolias, A. S., & Ma, W. J.
Affiliations
  • Baylor College of Medicine (US)
  • University of Rochester (US)
Venue
Proceedings of the National Academy of Sciences
Published
2013-11-22
Volume
110
Issue
50
Pages
20332-20337
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2141516486
Cited by
64
References
42
Citations by year
2026: 1 · 2025: 5 · 2024: 4 · 2023: 5 · 2022: 7 · 2021: 9 · 2020: 3 · 2019: 10 · 2018: 7 · 2017: 3 · 2016: 4 · 2015: 2 · 2014: 4
FWCI
1.83
Type
journal article
Language
en
Open access
Locations
  • Proceedings of the National Academy of Sciences (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
Innovative Research Group Project of the National Natural Science Foundation of China
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
Keywords
visual categorization, decision boundaries, sensory uncertainty, sensory noise, monkeys, neural network model, divisive normalization, perceptual decision making
MeSH terms
Animals, Haplorhini, Bayes Theorem, Concept Formation, Decision Making, Humans, Models, Neurological, Nerve Net, Species Specificity, Visual Perception, Likelihood Functions

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