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Cited by 342Visual perception and processing mechanismsDecision-Making and Behavioral EconomicsNeural and Behavioral Psychology Studies

Suboptimality in perceptual decision making

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Rahnev, Dobromir, Denison, Rachel N.·2018·Behavioral and Brain Sciences
DOI 10.1017/s0140525x18000936PMC PMC6110994

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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 Bayesian optimality predictions, researchers frequently appeal to auxiliary assumptions such as suboptimal priors, cognitive capacity limitations, or approximation algorithms to account for deviations.

    “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

    Section 5.1 says new findings are often modeled with flexibly chosen Bayesian components, and Section 3.8 describes priors invoked to explain perceptual biases. Section 4.2 also discusses computational capacities as a limit on optimality. The passages do not establish that researchers frequently invoke approximation algorithms specifically to explain deviations. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Alternative non-Bayesian models grounded in heuristics, dynamical systems, or adaptation mechanisms can often account for the same neural patterns with equal or greater parsimony and explanatory clarity.

    “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

    Section 5.3 argues that models should be judged by how well they describe behavior rather than by whether they support optimal decisions, and notes that heuristic rules can produce close-to-optimal behavior. Section 5.2 also leaves open the possibility of a non-Bayesian observer model. The cited passages do not establish that heuristic, dynamical-systems, or adaptation models account for the same neural patterns with equal or greater parsimony and explanatory clarity; section 3.8 instead discusses adaptation within a Bayesian framework. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Persistent deviations from Bayesian-optimal behavior in tightly controlled tasks, unexplainable by noise, heuristics, or approximation, would falsify normative claims of the Bayesian brain hypothesis.

    “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

    Section 5.3 says the many findings of suboptimality make the assumption that the brain is built for optimal decisions suspect. Section 4.2 says deviations can challenge components of a specified model, but it does not say they would falsify normative Bayesian claims generally. Section 2.2 also distinguishes experimentally defined priors from likelihood functions derived under assumptions, so the claim overstates how fully the tasks define both. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Different suboptimalities may coexist, and the same kind of computational constraints that affect first-order cognition are likely to affect the suboptimality of metacognition.

    “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

    Section 3.5 reports several sources of suboptimal confidence judgments, and §4.2 says computational complexity can make optimal perceptual decisions unachievable. The copy does not establish that different suboptimalities coexist in the same decision or that the same computational constraints affect first-order cognition and metacognition. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • The meaningfulness of a Bayes-optimal result is questionable and the use of Bayesian modelling is disputed.

    “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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Related

Authors
Rahnev, D., & Denison, R. N.
Affiliations
  • Georgia Institute of Technology (US)
  • New York University (US)
ORCID
Venue
Behavioral and Brain Sciences
Publisher
Cambridge University Press (CUP)
Published
2018
Volume
41
Pages
e223-e223
ISSN
  • 0140-525X (ISSN-L)
  • 1469-1825
DOI
10.1017/s0140525x18000936
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 · 2017: 2
FWCI
20.70
Type
journal article
Language
en
Open access
Licence
www.cambridge.org/core/terms (unspecified, 57 d)
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

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