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Neural Systems Responding to Degrees of Uncertainty in Human Decision-Making
Abstract
Much is known about how people make decisions under varying levels of probability (risk). Less is known about the neural basis of decision-making when probabilities are uncertain because of missing information (ambiguity). In decision theory, ambiguity about probabilities should not affect choices. Using functional brain imaging, we show that the level of ambiguity in choices correlates positively with activation in the amygdala and orbitofrontal cortex, and negatively with a striatal system. Moreover, striatal activity correlates positively with expected reward. Neurological subjects with orbitofrontal lesions were insensitive to the level of ambiguity and risk in behavioral choices. These data suggest a general neural circuit responding to degrees of uncertainty, contrary to decision theory.
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“These two dimensions, which characterize most daily life events, have been shown to involve the modulation of amygdala, prefrontal cortex and striatum (e.g., Hsu et al., 2005), the key nodes of the triadic model.”
From A systematic review of fMRI reward paradigms used in studies of adolescents vs. adults: The impact of task design and implications for understanding neurodevelopment (2013) · cites this paper as Hsu et al., 2005
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“recent theories of amygdala function argue that the amygdala subserves an abstract function in detecting uncertainty (13) or ambiguity (14) in the environment and in triggering arousal and vigilance as a consequence.”
From Amygdala damage eliminates monetary loss aversion (2010) · cites this paper as [14]
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“The caudate has been reported in the monetary risk assessment literature as differentially encoding gains and losses (Seymour, Daw, Dayan, Singer, & Dolan, 2007), characterizing expected reward and reward variance reflecting risk (Preuschoff, Bossaerts, & Quartz, 2006), and being active during risk conditions as compa…”
BackgroundInfluentialChung, D., Yun, K., Kim, J. H., Jang, B., & Jeong, J. (2011) · PLoS ONE · doi:10.1371/journal.pone.0017044 · Cited by 19
Yan, Z., Witthöft, M., Bailer, J., Diener, C., & Mier, D. (2019) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/s00406-017-0832-8 · Cited by 19
Zhang, X., Wang, X., Dong, D., Sun, X., Zhong, X., Xiong, G., Cheng, C., Lei, H., Chai, Y., Yu, M., et al. (2023) · Biological Psychiatry · doi:10.1016/j.biopsych.2022.09.003 · Cited by 11
Metadata
- Authors
- Hsu, M., Bhatt, M., Adolphs, R., Tranel, D., & Camerer, C. F.
- Affiliations
- California Institute of Technology (US)
- Corresponding author
- Camerer, Colin F.
- Venue
- Science
- Published
- 2005-12-09
- Volume
- 310
- Issue
- 5754
- Pages
- 1680-1683
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2104346961
- Cited by
- 1,411
- References
- 29
- Citations by year
- 2026: 48 · 2025: 33 · 2024: 88 · 2023: 41 · 2022: 62 · 2021: 57 · 2020: 47 · 2019: 54 · 2018: 65 · 2017: 76 · 2016: 68 · 2015: 79 · 2014: 85 · 2013: 85 · 2012: 87
- FWCI
- 29.46
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- Funders
- National Institute of Mental Health (R01MH067681)
- National Institute of Neurological Disorders and Stroke (P01 NS19632, P01NS019632)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Keywords
- decision making under ambiguity, risk, uncertainty, amygdala, orbitofrontal cortex, striatum, expected reward, neural basis of choice, functional neuroimaging
- MeSH terms
- Adult, Amygdala, Brain, Brain Diseases, Brain Mapping, Corpus Striatum, Decision Making, Decision Theory, Female, Frontal Lobe, Games, Experimental, Humans, Magnetic Resonance Imaging, Male, Mental Processes, Probability, Reward, Risk, Confidence Intervals, Likelihood Functions, Uncertainty
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