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  Observing the observer (I): Meta-Bayesian models of learning and decision-making

Daunizeau, J., den Ouden, H. E. M., Pessiglione, M., Kiebel, S. J., Stephan, K. E., & Friston, K. J. (2010). Observing the observer (I): Meta-Bayesian models of learning and decision-making. PLoS One, 5(12): e15554. doi:10.1371/journal.pone.0015554.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0012-10B3-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-ED60-7
Genre: Journal Article

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© 2010 Daunizeau et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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 Creators:
Daunizeau, Jean1, 2, Author
den Ouden, Hanneke E. M.3, Author
Pessiglione, Mathias4, Author
Kiebel, Stefan J.5, Author              
Stephan, Klaas E.1, 2, Author
Friston, Karl J.1, Author
Affiliations:
1Wellcome Trust Centre for Neuroimaging, University College of London, United Kingdom, ou_persistent22              
2Laboratory for Social and Neural Systems Research, Institute of Empirical Research in Economics, University of Zurich, Switzerland , ou_persistent22              
3Donders Institute for Brain, Cognition and Behaviour, Nijmegen, the Netherlands , ou_persistent22              
4Brain and Spine Institute, Hôpital Pitié-Salpêtrière, Paris, France, ou_persistent22              
5Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              

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 Abstract: In this paper, we present a generic approach that can be used to infer how subjects make optimal decisions under uncertainty. This approach induces a distinction between a subject’s perceptual model, which underlies the representation of a hidden ‘‘state of affairs’’ and a response model, which predicts the ensuing behavioural (or neurophysiological) responses to those inputs. We start with the premise that subjects continuously update a probabilistic representation of the causes of their sensory inputs to optimise their behaviour. In addition, subjects have preferences or goals that guide decisions about actions given the above uncertain representation of these hidden causes or state of affairs. From a Bayesian decision theoretic perspective, uncertain representations are so-called ‘‘posterior’’ beliefs, which are influenced by subjective ‘‘prior’’ beliefs. Preferences and goals are encoded through a ‘‘loss’’ (or ‘‘utility’’) function, which measures the cost incurred by making any admissible decision for any given (hidden) state of affair. By assuming that subjects make optimal decisions on the basis of updated (posterior) beliefs and utility (loss) functions, one can evaluate the likelihood of observed behaviour. Critically, this enables one to ‘‘observe the observer’’, i.e. identify (context- or subject-dependent) prior beliefs and utility-functions using psychophysical or neurophysiological measures. In this paper, we describe the main theoretical components of this meta-Bayesian approach (i.e. a Bayesian treatment of Bayesian decision theoretic predictions). In a companion paper (‘Observing the observer (II): deciding when to decide’), we describe a concrete implementation of it and demonstrate its utility by applying it to simulated and real reaction time data from an associative learning task.

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Language(s): eng - English
 Dates: 2010-11-122010-12-14
 Publication Status: Published online
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 Identifiers: DOI: 10.1371/journal.pone.0015554
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Title: PLoS One
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Sciene
Pages: - Volume / Issue: 5 (12) Sequence Number: e15554 Start / End Page: - Identifier: ISSN: 1932-6203
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000277850