An Empirical Test of the Role of Value Certainty in Decision Making.

certainty choice (selection) models choice confidence decision making metacognition preference change through choice subjective value

Journal

Frontiers in psychology
ISSN: 1664-1078
Titre abrégé: Front Psychol
Pays: Switzerland
ID NLM: 101550902

Informations de publication

Date de publication:
2020
Historique:
received: 19 06 2020
accepted: 31 08 2020
entrez: 16 11 2020
pubmed: 17 11 2020
medline: 17 11 2020
Statut: epublish

Résumé

Most contemporary models of value-based decisions are built on value estimates that are typically self-reported by the decision maker. Such models have been successful in accounting for choice accuracy and response time, and more recently choice confidence. The fundamental driver of such models is choice difficulty, which is almost always defined as the absolute value difference between the subjective value ratings of the options in a choice set. Yet a decision maker is not necessarily able to provide a value estimate with the same degree of certainty for each option that he encounters. We propose that choice difficulty is determined not only by absolute value distance of choice options, but also by their value certainty. In this study, we first demonstrate the reliability of the concept of an option-specific value certainty using three different experimental measures. We then demonstrate the influence that value certainty has on choice, including accuracy (consistency), choice confidence, response time, and choice-induced preference change (i.e., the degree to which value estimates change from pre- to post-choice evaluation). We conclude with a suggestion of how popular contemporary models of choice (e.g., race model, drift-diffusion model) could be improved by including option-specific value certainty as one of their inputs.

Identifiants

pubmed: 33192874
doi: 10.3389/fpsyg.2020.574473
pmc: PMC7605174
doi:

Types de publication

Journal Article

Langues

eng

Pagination

574473

Informations de copyright

Copyright © 2020 Lee and Coricelli.

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Auteurs

Douglas Lee (D)

Department of Economics, University of Southern California, Los Angeles, CA, United States.

Giorgio Coricelli (G)

Department of Economics, University of Southern California, Los Angeles, CA, United States.

Classifications MeSH