The Neural Representation of Unexpected Uncertainty during Value-Based Decision Making
Uncertainty is an inherent property of the environment and a central feature of models of decision-making and learning. Theoretical propositions suggest that one form, unexpected uncertainty, may be used to rapidly adapt to changes in the environment, while being influenced by two other forms: risk and estimation uncertainty. While previous studies have reported neural representations of estimation uncertainty and risk, relatively little is known about unexpected uncertainty. Here, participants performed a decision-making task while undergoing functional magnetic resonance imaging (fMRI), which, in combination with a Bayesian model-based analysis, enabled us to separately examine each form of uncertainty examined. We found representations of unexpected uncertainty in multiple cortical areas, as well as the noradrenergic brainstem nucleus locus coeruleus. Other unique cortical regions were found to encode risk, estimation uncertainty, and learning rate. Collectively, these findings support theoretical models in which several formally separable uncertainty computations determine the speed of learning.
Additional Information© 2013 Elsevier Inc. Accepted: April 26, 2013; Published: July 10, 2013. This work was supported by the Science Foundation Ireland grant 08/IN.1/B1844 and a European Research Council grant (to J.O.D). P.B. and E.P.L.N. acknowledge the support of the Swiss Finance Institute to the Ecole Polytechnique Fédérale Lausanne.
Accepted Version - nihms788029.pdf