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On why we lack confidence in some signal-detection-based analyses of confidence

Arnold, D. H., Johnston, A., Adie, J. & Yarrow, K. ORCID: 0000-0003-0666-2163 (2023). On why we lack confidence in some signal-detection-based analyses of confidence. Consciousness and Cognition, 113, article number 103532. doi: 10.1016/j.concog.2023.103532


Signal-detection theory (SDT) is one of the most popular frameworks for analyzing data from studies of human behavior – including investigations of confidence. SDT-based analyses of confidence deliver both standard estimates of sensitivity (d’), and a second estimate informed by high-confidence decisions – meta d’. The extent to which meta d’ estimates fall short of d’ estimates is regarded as a measure of metacognitive inefficiency. These analyses rely on a key but questionable assumption – that repeated exposures to an input will evoke a normally-shaped distribution of perceptual experiences (the normality assumption). We show that when distributions of experience do not conform with the normality assumption, meta d’ can be systematically underestimated relative to d'. We explain why violations of the normality assumption are especially a problem for some popular SDT-based analyses of confidence, in contrast to other analyses inspired by the SDT framework, which are more robust.

Publication Type: Article
Additional Information: © 2023. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Publisher Keywords: Perceptual metacognition; Confidence; Visual Adaptation; Signal Detection Theory
Subjects: B Philosophy. Psychology. Religion > BF Psychology
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Departments: School of Health & Psychological Sciences > Psychology
SWORD Depositor:
[thumbnail of Direction_AE_Confidence_CC_rnd3.pdf] Text - Accepted Version
This document is not freely accessible until 7 June 2024 due to copyright restrictions.
Available under License Creative Commons Attribution Non-commercial No Derivatives.

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