Not Noisy, Just Wrong: The Role of Suboptimal Inference in Behavioral Variability
Open accessAbstract
Behavior varies from trial to trial even when the stimulus is maintained as constant as possible. In many models, this variability is attributed to noise in the brain. Here, we propose that there is another major source of variability: suboptimal inference. Importantly, we argue that in most tasks of interest, and particularly complex ones, suboptimal inference is likely to be the dominant component of behavioral variability. This perspective explains a variety of intriguing observations, including why variability appears to be larger on the sensory than on the motor side, and why our sensors are sometimes surprisingly unreliable.
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The representational capacity required for full Bayesian inference exceeds what could plausibly be implemented in neural tissue, particularly for continuous variables with theoretically infinite possible states.
“The representational capacity required exceeds what could plausibly be implemented in neural tissue, particularly for continuous variables with theoretically infinite possible states (Beck et al. 2012; Fiser et al. 2010).”
From The myth of the Bayesian brain (2025) · cites this paper as Beck et al. 2012
In “Why Suboptimal Inference Is Inevitable” (page unavailable), the paper says optimal Bayesian inference requires computing a posterior distribution and that exact inference for many problems can be computationally intractable. It does not attribute that difficulty to insufficient neural representational capacity or discuss continuous variables having infinitely many possible states. (LLM, conf=0.84)
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Self-citation
- Authors
- Beck, J. M., Ma, W. J., Pitkow, X., Latham, P. E., & Pouget, A.
- Affiliations
- University of Rochester (US)
- Baylor College of Medicine (US)
- Gatsby Computational Neuroscience Unit (GB)
- University of Geneva (CH)
- Corresponding author
- Pouget, Alexandre
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2012-04
- Volume
- 74
- Issue
- 1
- Pages
- 30-39
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC4486264
- OpenAlex
- W2144984120
- Cited by
- 316
- References
- 70
- Citations by year
- 2026: 10 · 2025: 19 · 2024: 25 · 2023: 12 · 2022: 26 · 2021: 22 · 2020: 36 · 2019: 31 · 2018: 22 · 2017: 24 · 2016: 24 · 2015: 22 · 2014: 29 · 2013: 11 · 2012: 3
- FWCI
- 10.51
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- Archive ouverte UNIGE (University of Geneva) (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Archive ouverte UNIGE (University of Geneva) (submittedVersion, cc-by)
- Funders
- National Science Foundation (0446730, 1132009)
- James S. McDonnell Foundation
- Multidisciplinary University Research Initiative (N00014-07-1-0937)
- National Eye Institute (R01EY020958)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Keywords
- behavioral variability, trial-to-trial variability, neural noise, sensory uncertainty, sensor reliability, Bayesian inference, perceptual decision making
- MeSH terms
- Animals, Behavior, Brain, Field Dependence-Independence, Humans, Models, Neurological, Models, Psychological, Neurons, Perceptual Masking, Sensation, Reproducibility of Results, Uncertainty, Behavioral Research
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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.
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