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Uncertainty, Neuromodulation, and Attention
Open accessAbstract
Uncertainty in various forms plagues our interactions with the environment. In a Bayesian statistical framework, optimal inference and prediction, based on unreliable observations in changing contexts, require the representation and manipulation of different forms of uncertainty. We propose that the neuromodulators acetylcholine and norepinephrine play a major role in the brain's implementation of these uncertainty computations. Acetylcholine signals expected uncertainty, coming from known unreliability of predictive cues within a context. Norepinephrine signals unexpected uncertainty, as when unsignaled context switches produce strongly unexpected observations. These uncertainty signals interact to enable optimal inference and learning in noisy and changeable environments. This formulation is consistent with a wealth of physiological, pharmacological, and behavioral data implicating acetylcholine and norepinephrine in specific aspects of a range of cognitive processes. Moreover, the model suggests a class of attentional cueing tasks that involve both neuromodulators and shows how their interactions may be part-antagonistic, part-synergistic.
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Metadata
- Authors
- Yu, A. J., & Dayan, P.
- Affiliations
- Gatsby Computational Neuroscience Unit (GB)
- Corresponding author
- Yu, Angela J.
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2005-05
- Volume
- 46
- Issue
- 4
- Pages
- 681-692
- ISSN
- 0896-6273 (ISSN-L)
- OpenAlex
- W2154562678
- Cited by
- 1,774
- References
- 97
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- FWCI
- 19.61
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
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- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 2999 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
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- TUbilio (Technical University of Darmstadt) (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Keywords
- uncertainty, neuromodulation, acetylcholine, norepinephrine, attention, Bayesian inference, context switching, attentional cueing, prediction, noisy environments, learning
- MeSH terms
- Acetylcholine, Algorithms, Animals, Attention, Bayes Theorem, Computer Simulation, Dose-Response Relationship, Drug, Generalization, Psychological, Humans, Learning, Models, Neurological, Nicotine, Norepinephrine, Scopolamine, Time Factors, Likelihood Functions, Signal Detection, Psychological, Nicotinic Agonists, Muscarinic Antagonists, Maze Learning, Uncertainty, Rats
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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