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  Observing the observer (II): Deciding when to decide

Daunizeau, J., den Ouden, H. E. M., Pessiglione, M., Kiebel, S. J., Friston, K. J., & Stephan, K. E. (2010). Observing the observer (II): Deciding when to decide. PLoS One, 5(12): e15555. doi:10.1371/journal.pone.0015555.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0011-27D6-9 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-002B-F930-6
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              
Friston, Karl J.1, Author
Stephan, Klaas E.1, 2, Author
Affiliations:
1Laboratory for Social and Neural Systems Research, Institute of Empirical Research in Economics, University of Zurich, Switzerland, ou_persistent22              
2Donders Institute for Brain, Cognition and Behaviour, Nijmegen, the Netherlands , ou_persistent22              
3Brain and Spine Institute, Hôpital Pitié-Salpêtrière, Paris, France, ou_persistent22              
4Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
5Wellcome Trust Centre for Neuroimaging, University College of London, United Kingdom, ou_persistent22              

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 Abstract: In a companion paper [1], we have presented a generic approach for inferring how subjects make optimal decisions under uncertainty. From a Bayesian decision theoretic perspective, uncertain representations correspond to ‘‘posterior’’ beliefs, which result from integrating (sensory) information with 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 or unknown) state of the world. 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. In this paper, we describe a concrete implementation of this meta-Bayesian approach (i.e. a Bayesian treatment of Bayesian decision theoretic predictions) and demonstrate its utility by applying it to both simulated and empirical reaction time data from an associative learning task. Here, inter-trial variability in reaction times is modelled as reflecting the dynamics of the subjects’ internal recognition process, i.e. the updating of representations (posterior densities) of hidden states over trials while subjects learn probabilistic audio-visual associations. We use this paradigm to demonstrate that our meta-Bayesian framework allows for (i) probabilistic inference on the dynamics of the subject’s representation of environmental states, and for (ii) model selection to disambiguate between alternative preferences (loss functions) human subjects could employ when dealing with trade-offs, such as between speed and accuracy. Finally, we illustrate how our approach can be used to quantify subjective beliefs and preferences that underlie inter-individual differences in behaviour.

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Language(s): eng - English
 Dates: 2010-11-122010-12-14
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Method: -
 Identifiers: eDoc: 537845
DOI: 10.1371/journal.pone.0015555
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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: e15555 Start / End Page: - Identifier: ISSN: 1932-6203
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000277850