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Suboptimality in perceptual decision making
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Abstract
Human perceptual decisions are often described as optimal. Critics of this view have argued that claims of optimality are overly flexible and lack explanatory power. Meanwhile, advocates for optimality have countered that such criticisms single out a few selected papers. To elucidate the issue of optimality in perceptual decision making, we review the extensive literature on suboptimal performance in perceptual tasks. We discuss eight different classes of suboptimal perceptual decisions, including improper placement, maintenance, and adjustment of perceptual criteria; inadequate tradeoff between speed and accuracy; inappropriate confidence ratings; misweightings in cue combination; and findings related to various perceptual illusions and biases. In addition, we discuss conceptual shortcomings of a focus on optimality, such as definitional difficulties and the limited value of optimality claims in and of themselves. We therefore advocate that the field drop its emphasis on whether observed behavior is optimal and instead concentrate on building and testing detailed observer models that explain behavior across a wide range of tasks. To facilitate this transition, we compile the proposed hypotheses regarding the origins of suboptimal perceptual decisions reviewed here. We argue that verifying, rejecting, and expanding these explanations for suboptimal behavior – rather than assessing optimality per se – should be among the major goals of the science of perceptual decision making.
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“when behavioral data fail to conform to the predictions of Bayesian optimality, researchers frequently appeal to auxiliary assumptions—such as “suboptimal” priors, cognitive capacity limitations, or the use of approximation algorithms—to account for these deviations (Rahnev and Denison 2018; Sanborn and Chater 2016).”
From The myth of the Bayesian brain (2025) · cites this paper as Rahnev and Denison 2018
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“Moreover, alternative non-Bayesian models—grounded in heuristics, dynamical systems, or adaptation mechanisms—can often account for the same neural patterns with equal or even greater parsimony and explanatory clarity (Rahnev and Denison 2018).”
From The myth of the Bayesian brain (2025) · cites this paper as Rahnev and Denison 2018
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“In tightly controlled tasks where prior probabilities and likelihoods are explicitly defined, persistent deviations from Bayesian-optimal behavior—unexplainable by noise, heuristics, or approximation—would falsify normative claims (Rahnev and Denison 2018).”
From The myth of the Bayesian brain (2025) · cites this paper as Rahnev and Denison 2018
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“More generally, different suboptimalities may coexist, and the same kind of computational constraints that affect first-order cognition are likely to affect the suboptimality of metacognition (Rahnev & Denison 2018).”
From Metacognition and Confidence: A Review and Synthesis (2024) · cites this paper as Rahnev & Denison 2018
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“However, the meaningfulness of a Bayes-optimal result is questionable [48] and the use of Bayesian modelling is disputed [49–53].”
From A model of cue integration as vector summation in the insect brain (2023) · cites this paper as [52]
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Self-citation
Metadata
- Authors
- Rahnev, D., & Denison, R. N.
- Affiliations
- Georgia Institute of Technology (US)
- New York University (US)
- Venue
- Behavioral and Brain Sciences
- Publisher
- Cambridge University Press (CUP)
- Published
- 2018
- Volume
- 41
- Pages
- e223-e223
- ISSN
- 0140-525X (ISSN-L)
- 1469-1825
- PMC
- PMC6110994
- OpenAlex
- W2790916085
- Cited by
- 342
- References
- 320
- Citations by year
- 2026: 23 · 2025: 36 · 2024: 34 · 2023: 62 · 2022: 45 · 2021: 42 · 2020: 37 · 2019: 48 · 2018: 12
- FWCI
- 20.70
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Behavioral and Brain Sciences (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Eye Institute (F32EY025533, T32EY007136)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- perceptual decision making, suboptimal performance, principle of optimality, speed-accuracy trade-off, confidence ratings, cue combination, perceptual illusions, perceptual bias, model observer
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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