ArticlePLoS computational biology2022
Understanding the structure of cognitive noise.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- The samplr package: A tool for modeling human cognition with sampling algorithms.Behavior research methods · 2026Article
- Generated outcomes in risky choice reveal biased sampling and sequential dependencies.Communications psychology · 2026Article
- Noise in Cognition: Bug or Feature?Perspectives on psychological science : a journal of the Association for Psychological Science · 2025Article
- Neural decoding of competitive decision-making in Rock-Paper-Scissors.Social cognitive and affective neuroscience · 2025Article
- The autocorrelated Bayesian sampler: A rational process for probability judgments, estimates, confidence intervals, choices, confidence judgments, and response times.Psychological review · 2024Article
- Explaining the flaws in human random generation as local sampling with momentum.PLoS computational biology · 2024Article
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Authors and funding
4 authors.
Funding
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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.
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Registered trials
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