The precision test of metacognitive sensitivity and confidence criteria.

Confidence Perceptual metacognition Signal detection theory

Journal

Consciousness and cognition
ISSN: 1090-2376
Titre abrégé: Conscious Cogn
Pays: United States
ID NLM: 9303140

Informations de publication

Date de publication:
16 Jul 2024
Historique:
received: 16 10 2023
revised: 24 06 2024
accepted: 02 07 2024
medline: 18 7 2024
pubmed: 18 7 2024
entrez: 17 7 2024
Statut: aheadofprint

Résumé

Humans experience feelings of confidence in their decisions. In perception, these feelings are typically accurate - we tend to feel more confident about correct decisions. The degree of insight people have into the accuracy of their decisions is known as metacognitive sensitivity. Currently popular methods of estimating metacognitive sensitivity are subject to interpretive ambiguities because they assume people have normally shaped distributions of different experiences when they are repeatedly exposed to a single input. If this normality assumption is violated, calculations can erroneously underestimate metacognitive sensitivity. Here, we describe a means of estimating metacognitive sensitivity that is more robust to violations of the normality assumption. This improved method can easily be added to standard behavioral experiments, and the authors provide Matlab code to help researchers implement these analyses and experimental procedures.

Identifiants

pubmed: 39018832
pii: S1053-8100(24)00095-3
doi: 10.1016/j.concog.2024.103728
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

103728

Informations de copyright

Copyright © 2024. Published by Elsevier Inc.

Auteurs

Derek H Arnold (DH)

School of Psychology, The University of Queensland, Australia. Electronic address: d.arnold@psy.uq.edu.au.

Mitchell Clendinen (M)

School of Psychology, The University of Queensland, Australia.

Alan Johnston (A)

School of Psychology, The University of Nottingham, United Kingdom.

Alan L F Lee (ALF)

Department of Psychology, Lingnan University, Hong Kong.

Kielan Yarrow (K)

School of Psychology, City University London, United Kingdom.

Classifications MeSH