Decision theory: what “should” the nervous system do?
Abstract
The purpose of our nervous system is to allow us to successfully interact with our environment. This normative idea is formalized by decision theory that defines which choices would be most beneficial. We live in an uncertain world, and each decision may have many possible outcomes; choosing the best decision is thus complicated. Bayesian decision theory formalizes these problems in the presence of uncertainty and often provides compact models that predict observed behavior. With its elegant formalization of the problems faced by the nervous system, it promises to become a major inspiration for studies in neuroscience.
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Bayesian decision theory provides a general and often quantitatively accurate account of perceptual decisions in a wide variety of tasks.
“Bayesian decision theory provides a general and often quantitatively accurate account of perceptual decisions in a wide variety of tasks [16–18].”
From Comparing Bayesian and non-Bayesian accounts of human confidence reports (2018) · cites this paper as [18]
Bayesian decision theory identifies an optimal action as one that maximizes expected utility, where expected outcomes are weighted by their relative uncertainty.
“One framework to address this challenge is Bayesian decision theory (Körding, 2007; Dayan and Daw, 2008; Daunizeau et al., 2010). In a nutshell, this identifies an optimal action as one that maximizes the “expected utility” (where “expected” refers to a weighted average; i.e., the predicted outcomes are weighted by their relative uncertainty).”
From Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression (2016) · cites this paper as Körding, 2007
There is a rich literature on the computation and usage of uncertainty to optimize decision-making that often remains separate from the literature on metacognition.
“There is a rich literature on the computation and usage of uncertainty to optimize decision-making [ 25 , 26 ], but one that often remains separate from the literature on metacognition.”
From Metacognition: computation, biology and function (2012) · cites this paper as [26]
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References 43 · Cited by 313
- Authors
- Körding, K.
- Affiliations
- Department of Physical Medicine and Rehabilitation, Department of Physiology, and Department of Applied Mathematics, Institute of Neuroscience, Northwestern University and Rehabilitation Institute of Chicago, Room O-922, 345 East Superior Street, Chicago, IL 60611, USA.
- Venue
- Science
- Published
- 2007-10-26
- Volume
- 318
- Issue
- 5850
- Pages
- 606-610
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2106539961
- Cited by
- 313
- References
- 43
- Citations by year
- 2026: 11 · 2025: 14 · 2024: 14 · 2023: 14 · 2022: 12 · 2021: 13 · 2020: 17 · 2019: 13 · 2018: 14 · 2017: 14 · 2016: 20 · 2015: 19 · 2014: 14 · 2013: 18 · 2012: 14
- FWCI
- 1.34
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Keywords
- decision theory, Bayesian decision theory, normative model, nervous system function, uncertainty, decision-making, behavior prediction, computational neuroscience
- MeSH terms
- Bayes Theorem, Brain, Decision Making, Humans, Nervous System Physiological Phenomena, Uncertainty
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