Differential representations of prior and likelihood uncertainty in the human brain

Iris Vilares, James D. Howard, Hugo L. Fernandes, Jay A. Gottfried, Konrad P. Kording

Research output: Contribution to journalArticlepeer-review

75 Scopus citations

Abstract

Background: Uncertainty shapes our perception of the world and the decisions we make. Two aspects of uncertainty are commonly distinguished: uncertainty in previously acquired knowledge (prior) and uncertainty in current sensory information (likelihood). Previous studies have established that humans can take both types of uncertainty into account, often in a way predicted by Bayesian statistics. However, the neural representations underlying these parameters remain poorly understood. Results: By varying prior and likelihood uncertainty in a decision-making task while performing neuroimaging in humans, we found that prior and likelihood uncertainty had quite distinct representations. Whereas likelihood uncertainty activated brain regions along the early stages of the visuomotor pathway, representations of prior uncertainty were identified in specialized brain areas outside this pathway, including putamen, amygdala, insula, and orbitofrontal cortex. Furthermore, the magnitude of brain activity in the putamen predicted individuals' personal tendencies to rely more on either prior or current information. Conclusions: Our results suggest different pathways by which prior and likelihood uncertainty map onto the human brain and provide a potential neural correlate for higher reliance on current or prior knowledge. Overall, these findings offer insights into the neural pathways that may allow humans to make decisions close to the optimal defined by a Bayesian statistical framework.

Original languageEnglish (US)
Pages (from-to)1641-1648
Number of pages8
JournalCurrent Biology
Volume22
Issue number18
DOIs
StatePublished - Sep 25 2012
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported by PhD grants from Fundacao para a Ciencia e Tecnologia SFRH/BD/33272/2007 (to I.V.), SFRH/BD/33525/2008 (to H.L.F.), National Institutes of Health (NIH) grants 2P01NS044393 and 1R01NS063399 (to K.P.K.), NIH grants 1R01DC010014 and K08DC007653 (to J.A.G.), and a Northwestern F32 Institutional Training Grant (to J.D.H.). We would like to thank Peter Dayan for helpful comments on the manuscript.

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