Human optional stopping in a heteroscedastic world

Hannah Tickle, Konstantinos Tsetsos, Maarten Speekenbrink, Christopher Summerfield

Research output: Contribution to journalArticle (Academic Journal)peer-review

3 Citations (Scopus)

Abstract

When making decisions, animals must trade off the benefits of information harvesting against the opportunity cost of prolonged deliberation. Deciding when to stop accumulating information and commit to a choice is challenging in natural environments, where the reliability of decision-relevant information may itself vary unpredictably over time (variable variance or "heteroscedasticity"). We asked humans to perform a categorization task in which discrete, continuously valued samples (oriented gratings) arrived in series until the observer made a choice. Human behavior was best described by a model that adaptively weighted sensory signals by their inverse prediction error and integrated the resulting quantities with a linear urgency signal to a decision threshold. This model approximated the output of a Bayesian model that computed the full posterior probability of a correct response, and successfully predicted adaptive weighting of decision information in neural signals. Adaptive weighting of decision information may have evolved to promote optional stopping in heteroscedastic natural environments. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Original languageEnglish
Pages (from-to)1-22
Number of pages22
JournalPsychological Review
Volume130
Issue number1
Early online date27 Sept 2021
DOIs
Publication statusPublished - 1 Jan 2023

Bibliographical note

Publisher Copyright:
© 2021 American Psychological Association

Structured keywords

  • Cognitive Neuroscience
  • Cognitive Science

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