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Arousal-related adjustments of perceptual biases optimize perception in dynamic environments
Open accessPublished version of a preprint
Abstract
Prior expectations can be used to improve perceptual judgments about ambiguous stimuli. However, little is known about if and how these improvements are maintained in dynamic environments in which the quality of appropriate priors changes from one stimulus to the next. Using a sound-localization task, we show that changes in stimulus predictability lead to arousal-mediated adjustments in the magnitude of prior-driven biases that optimize perceptual judgments about each stimulus. These adjustments depend on task-dependent changes in the relevance and reliability of prior expectations, which subjects update using both normative and idiosyncratic principles. The resulting variations in biases across task conditions and individuals are reflected in modulations of pupil diameter, such that larger stimulus-evoked pupil responses correspond to smaller biases. These results suggest a critical role for the arousal system in adjusting the strength of perceptual biases with respect to inferred environmental dynamics to optimize perceptual judgements.
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“By employing Pontryagin's minimum principle on the probability density function space [1-9], the solution that satisfies the optimality condition δ ˜ J/δ ˜ u = 0 is given by”
From Theoretical Analysis of Resource-Induced Phase Transitions in Estimation Strategies (2026) · cites this paper as [8]
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Cites
The Nature of Statistical Learning Theory
Vapnik, V. (2000) · Statistics for Engineering and Information Science · doi:10.1007/978-1-4757-3264-1 · Cited by 46,387
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Nonparametric permutation tests for functional neuroimaging: A primer with examples
Nichols, T. E., & Holmes, A. P. (2002) · Human Brain Mapping · doi:10.1002/hbm.1058 · Cited by 6,256
Individual differences in reasoning: Implications for the rationality debate?
Stanovich, K. E., & West, R. F. (2000) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00003435 · Cited by 4,481
Aston-Jones, G., & Cohen, J. D. (2005) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.28.061604.135709 · Cited by 4,472
The Bayesian brain: The role of uncertainty in neural coding and computation
Knill, D. C., & Pouget, A. (2004) · Trends in Neurosciences · doi:10.1016/j.tins.2004.10.007 · Cited by 2,588
Learning the value of information in an uncertain world
Behrens, T. E. J., Woolrich, M. W., Walton, M. E., & Rushworth, M. F. S. (2007) · Nature Neuroscience · doi:10.1038/nn1954 · Cited by 2,053
How to grow a mind: Statistics, structure, and abstraction
Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011) · Science · doi:10.1126/science.1192788 · Cited by 1,815
Uncertainty, Neuromodulation, and Attention
Yu, A. J., & Dayan, P. (2005) · Neuron · doi:10.1016/j.neuron.2005.04.026 · Cited by 1,774
Bar, M. (2004) · Nature Reviews Neuroscience · doi:10.1038/nrn1476 · Cited by 1,613
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Metadata
- Authors
- Krishnamurthy, K., Nassar, M. R., Sarode, S., & Gold, J. I.
- Affiliations
- University of Pennsylvania (US)
- Brown University (US)
- Corresponding author
- Gold, Joshua I.
- Venue
- Nature Human Behaviour
- Published
- 2017-05-08
- Volume
- 1
- Issue
- 6
- ISSN
- 2397-3374 (ISSN-L)
- PMC
- PMC5638136
- OpenAlex
- W2611597219
- Cited by
- 117
- References
- 65
- Citations by year
- 2026: 18 · 2025: 17 · 2024: 20 · 2023: 7 · 2022: 10 · 2021: 9 · 2020: 13 · 2019: 14 · 2018: 6 · 2017: 3
- FWCI
- 5.58
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Human Behaviour (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Science Foundation (1533623)
- National Institutes of Health (NSF 1533623)
- National Institute of Mental Health (F32MH102009)
- National Eye Institute (R01EY015260)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neuroscience and Music Perception
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Keywords
- arousal, perceptual bias, prior expectations, Bayesian inference, stimulus predictability, dynamic environments, sound localization, pupil size, pupil response, perceptual optimization, environmental dynamics
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