Comparing Bayesian and non-Bayesian accounts of human confidence reports

William T. Adler, Wei Ji Ma

Research output: Contribution to journalArticle

Abstract

Humans can meaningfully report their confidence in a perceptual or cognitive decision. It is widely believed that these reports reflect the Bayesian probability that the decision is correct, but this hypothesis has not been rigorously tested against non-Bayesian alternatives. We use two perceptual categorization tasks in which Bayesian confidence reporting requires subjects to take sensory uncertainty into account in a specific way. We find that subjects do take sensory uncertainty into account when reporting confidence, suggesting that brain areas involved in reporting confidence can access low-level representations of sensory uncertainty, a prerequisite of Bayesian inference. However, behavior is not fully consistent with the Bayesian hypothesis and is better described by simple heuristic models that use uncertainty in a non-Bayesian way. Both conclusions are robust to changes in the uncertainty manipulation, task, response modality, model comparison metric, and additional flexibility in the Bayesian model. Our results suggest that adhering to a rational account of confidence behavior may require incorporating implementational constraints.

Original languageEnglish (US)
Article numbere1006572
JournalPLoS Computational Biology
Volume14
Issue number11
DOIs
StatePublished - Nov 1 2018

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Uncertainty
Confidence
uncertainty
heuristics
brain
Model Comparison
Bayesian Model
Bayesian inference
Categorization
Modality
Manipulation
Brain
Flexibility
Human
Heuristics
Metric
Alternatives
decision
comparison
Model

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics

Cite this

Comparing Bayesian and non-Bayesian accounts of human confidence reports. / Adler, William T.; Ma, Wei Ji.

In: PLoS Computational Biology, Vol. 14, No. 11, e1006572, 01.11.2018.

Research output: Contribution to journalArticle

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