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Nat Commun. 2016 Aug 18;7:12400. doi: 10.1038/ncomms12400.

Optimal policy for value-based decision-making.

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Département des Neurosciences Fondamentales, University of Geneva, Rue Michel-Servet 1, Genève 1211, Switzerland.
Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, Massachusetts 02115, USA.
Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY USA.
Gatsby Computational Neuroscience Unit, University College of London, London, UK.


For decades now, normative theories of perceptual decisions, and their implementation as drift diffusion models, have driven and significantly improved our understanding of human and animal behaviour and the underlying neural processes. While similar processes seem to govern value-based decisions, we still lack the theoretical understanding of why this ought to be the case. Here, we show that, similar to perceptual decisions, drift diffusion models implement the optimal strategy for value-based decisions. Such optimal decisions require the models' decision boundaries to collapse over time, and to depend on the a priori knowledge about reward contingencies. Diffusion models only implement the optimal strategy under specific task assumptions, and cease to be optimal once we start relaxing these assumptions, by, for example, using non-linear utility functions. Our findings thus provide the much-needed theory for value-based decisions, explain the apparent similarity to perceptual decisions, and predict conditions under which this similarity should break down.

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