2013年7月16日火曜日

The Neural Representation of Unexpected Uncertainty during Value-Based Decision Making

Elise Payzan-LeNestour, Simon Dunne, Peter Bossaerts, John P. O'Doherty
Neuron, Volume 79, Issue 1, 10 July 2013, Pages 191–201

SimonくんのNeuron論文。
おめでとう!

ヒトは報酬学習において、三種類の不確実性を同時に推定し学習率を調整している。また、その三種類「リスク(報酬の分散)、推定した報酬確率の不安度、報酬確率が変わる可能性」はそれぞれ異なる脳領域で処理されている。 http://www.cell.com/neuron/abstract/S0896-6273(13)00368-1

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.

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