Rational regulation of learning dynamics by pupil-linked arousal systems
Open accessAbstract
The ability to make inferences about the current state of a dynamic process requires ongoing assessments of the stability and reliability of data generated by that process. We found that these assessments, as defined by a normative model, were reflected in nonluminance-mediated changes in pupil diameter of human subjects performing a predictive-inference task. Brief changes in pupil diameter reflected assessed instabilities in a process that generated noisy data. Baseline pupil diameter reflected the reliability with which recent data indicate the current state of the data-generating process and individual differences in expectations about the rate of instabilities. Together these pupil metrics predicted the influence of new data on subsequent inferences. Moreover, a task- and luminance-independent manipulation of pupil diameter predictably altered the influence of new data. Thus, pupil-linked arousal systems can help to regulate the influence of incoming data on existing beliefs in a dynamic environment.
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Deviations from expected uncertainty should increase learning rates.
“Here, deviations from expected uncertainty (“unexpected uncertainty”) should increase learning rates.8,12,13,15–18”
From Serotonin neurons modulate learning rate through uncertainty (2022) · cites this paper as [12]
When some amount of uncertainty is expected, learning rates should decrease.
“When some amount of uncertainty is expected (also referred to as outcome variance or risk), learning rates should decrease.10–14”
From Serotonin neurons modulate learning rate through uncertainty (2022) · cites this paper as [12]
The learning rate β depends on global task properties such as volatility or uncertainty and is suggested to be modulated by noradrenergic inputs.
“This rate depends on global task properties such as the volatility or uncertainty of a given task, and has been suggested to be modulated by noradrenergic inputs (Cohen et al., 2007; Nassar et al., 2012).”
From Attention, learning, and the value of information (2012) · cites this paper as Nassar et al., 2012
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- Authors
- Nassar, M. R., Rumsey, K. M., Wilson, R. C., Parikh, K., Heasly, B., & Gold, J. I.
- Affiliations
- University of Pennsylvania (US)
- Princeton University (US)
- Corresponding author
- Gold, Joshua I
- Venue
- Nature Neuroscience
- Published
- 2012-06-03
- Volume
- 15
- Issue
- 7
- Pages
- 1040-1046
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- PMC
- PMC3386464
- OpenAlex
- W2008497075
- Cited by
- 722
- References
- 35
- Citations by year
- 2026: 67 · 2025: 69 · 2024: 71 · 2023: 52 · 2022: 53 · 2021: 63 · 2020: 70 · 2019: 53 · 2018: 45 · 2017: 50 · 2016: 50 · 2015: 25 · 2014: 30 · 2013: 19 · 2012: 4
- FWCI
- 12.79
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- McKnight Foundation
- University of Pennsylvania
- Wellcome Trust
- National Institute of Mental Health (F31MH093099, R21MH093904)
- National Institute on Drug Abuse (T90 DA22763, T90DA022763)
- National Eye Institute (R01EY015260)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
- Keywords
- pupil size, belief updating, predictive inference, learning dynamics, data reliability assessment, normative model, noisy data, Bayesian inference
- MeSH terms
- Adult, Arousal, Female, Humans, Learning, Male, Photic Stimulation, Psychomotor Performance, Pupil, Young Adult
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