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Understanding the structure of cognitive noise

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Zhu , Jian-Qiao , León-Villagrá, Pablo, Chater, Nick and Sanborn, Adam N. (2022) Understanding the structure of cognitive noise. PLoS Computational Biology, 18 (8). e1010312. doi:10.1371/journal.pcbi.1010312 ISSN 1553-7358.

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Official URL: https://doi.org/10.1371/journal.pcbi.1010312

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Abstract

Human cognition is fundamentally noisy. While routinely regarded as a nuisance in experimental investigation, the few studies investigating properties of cognitive noise have found surprising structure. A first line of research has shown that inter-response-time distributions are heavy-tailed. That is, response times between subsequent trials usually change only a small amount, but with occasional large changes. A second, separate, line of research has found that participants’ estimates and response times both exhibit long-range autocorrelations (i.e., 1/f noise). Thus, each judgment and response time not only depends on its immediate predecessor but also on many previous responses. These two lines of research use different tasks and have distinct theoretical explanations: models that account for heavy-tailed response times do not predict 1/f autocorrelations and vice versa. Here, we find that 1/f noise and heavy-tailed response distributions co-occur in both types of tasks. We also show that a statistical sampling algorithm, developed to deal with patchy environments, generates both heavy-tailed distributions and 1/f noise, suggesting that cognitive noise may be a functional adaptation to dealing with a complex world.

Item Type: Journal Article
Subjects: B Philosophy. Psychology. Religion > BF Psychology
Q Science > QP Physiology
Divisions: Faculty of Science, Engineering and Medicine > Science > Psychology
Faculty of Social Sciences > Warwick Business School
Library of Congress Subject Headings (LCSH): Cognition, Decision making, Human behavior
Journal or Publication Title: PLoS Computational Biology
Publisher: Public Library of Science
ISSN: 1553-7358
Official Date: 17 August 2022
Dates:
DateEvent
17 August 2022Published
16 June 2022Accepted
Volume: 18
Number: 8
Article Number: e1010312
DOI: 10.1371/journal.pcbi.1010312
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access (Creative Commons)
Date of first compliant deposit: 24 August 2022
Date of first compliant Open Access: 24 August 2022

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