Interacting with volatile environments stabilizes hidden-state inference and its brain signatures

Aurélien Weiss (), Valérian Chambon, Junseok K. Lee, Jan Drugowitsch and Valentin Wyart ()
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Aurélien Weiss: Institut National de la Santé et de la Recherche Médicale (Inserm)
Valérian Chambon: Université PSL
Junseok K. Lee: Institut National de la Santé et de la Recherche Médicale (Inserm)
Jan Drugowitsch: Harvard Medical School
Valentin Wyart: Institut National de la Santé et de la Recherche Médicale (Inserm)

Nature Communications, 2021, vol. 12, issue 1, 1-17

Abstract: Abstract Making accurate decisions in uncertain environments requires identifying the generative cause of sensory cues, but also the expected outcomes of possible actions. Although both cognitive processes can be formalized as Bayesian inference, they are commonly studied using different experimental frameworks, making their formal comparison difficult. Here, by framing a reversal learning task either as cue-based or outcome-based inference, we found that humans perceive the same volatile environment as more stable when inferring its hidden state by interaction with uncertain outcomes than by observation of equally uncertain cues. Multivariate patterns of magnetoencephalographic (MEG) activity reflected this behavioral difference in the neural interaction between inferred beliefs and incoming evidence, an effect originating from associative regions in the temporal lobe. Together, these findings indicate that the degree of control over the sampling of volatile environments shapes human learning and decision-making under uncertainty.

Date: 2021
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DOI: 10.1038/s41467-021-22396-6

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