A Bayesian model of context-sensitive value attribution

Rigoli, Francesco, Friston, Karl J, Martinelli, Cristina, Selakovic, Mirjana, Shergill, Sukhwinder S and Dolan, Raymond J (2016) A Bayesian model of context-sensitive value attribution. eLife, 5, e16127. ISSN (online) 2050-084X

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Abstract

Substantial evidence indicates that incentive value depends on an anticipation of rewards within a given context. However, the computations underlying this context sensitivity remain unknown. To address this question, we introduce a normative (Bayesian) account of how rewards map to incentive values. This assumes that the brain inverts a model of how rewards are generated. Key features of our account include (i) an influence of prior beliefs about the context in which rewards are delivered (weighted by their reliability in a Bayes-optimal fashion), (ii) the notion that incentive values correspond to precision-weighted prediction errors, (iii) and contextual information unfolding at different hierarchical levels. This formulation implies that incentive value is intrinsically context-dependent. We provide empirical support for this model by showing that incentive value is influenced by context variability and by hierarchically nested contexts. The perspective we introduce generates new empirical predictions that might help explaining psychopathologies, such as addiction.

Item Type: Article
Additional Information: This work was supported by the Wellcome Trust [grant number 098362/Z/12/Z], the European Research Council and the National Institute for Health Research Biomedical Research Centre.
Research Area: Psychology
Faculty, School or Research Centre: Faculty of Arts and Social Sciences > School of Law, Social and Behavioural Sciences
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Depositing User: Anna Englund
Date Deposited: 19 Feb 2019 11:22
Last Modified: 19 Feb 2019 11:22
DOI: https://doi.org/10.7554/eLife.16127
URI: http://eprints.kingston.ac.uk/id/eprint/42666

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