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Observing the Observer (II): Deciding When to Decide

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Listed:
  • Jean Daunizeau
  • Hanneke E M den Ouden
  • Matthias Pessiglione
  • Stefan J Kiebel
  • Karl J Friston
  • Klaas E Stephan

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.

Suggested Citation

  • Jean Daunizeau & Hanneke E M den Ouden & Matthias Pessiglione & Stefan J Kiebel & Karl J Friston & Klaas E Stephan, 2010. "Observing the Observer (II): Deciding When to Decide," PLOS ONE, Public Library of Science, vol. 5(12), pages 1-19, December.
  • Handle: RePEc:plo:pone00:0015555
    DOI: 10.1371/journal.pone.0015555
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    References listed on IDEAS

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    1. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    2. Jean Daunizeau & Hanneke E M den Ouden & Matthias Pessiglione & Stefan J Kiebel & Klaas E Stephan & Karl J Friston, 2010. "Observing the Observer (I): Meta-Bayesian Models of Learning and Decision-Making," PLOS ONE, Public Library of Science, vol. 5(12), pages 1-10, December.
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    Cited by:

    1. Dimitrije Marković & Jan Gläscher & Peter Bossaerts & John O’Doherty & Stefan J Kiebel, 2015. "Modeling the Evolution of Beliefs Using an Attentional Focus Mechanism," PLOS Computational Biology, Public Library of Science, vol. 11(10), pages 1-34, October.
    2. Mateus Joffily & Giorgio Coricelli, 2013. "Emotional valence and the free-energy principle," Post-Print halshs-00862392, HAL.
    3. Andreea O Diaconescu & Christoph Mathys & Lilian A E Weber & Jean Daunizeau & Lars Kasper & Ekaterina I Lomakina & Ernst Fehr & Klaas E Stephan, 2014. "Inferring on the Intentions of Others by Hierarchical Bayesian Learning," PLOS Computational Biology, Public Library of Science, vol. 10(9), pages 1-19, September.
    4. Changbo Zhu & Ke Zhou & Fengzhen Tang & Yandong Tang & Xiaoli Li & Bailu Si, 2022. "A Hierarchical Bayesian Model for Inferring and Decision Making in Multi-Dimensional Volatile Binary Environments," Mathematics, MDPI, vol. 10(24), pages 1-35, December.
    5. Marie Devaine & Jean Daunizeau, 2017. "Learning about and from others' prudence, impatience or laziness: The computational bases of attitude alignment," PLOS Computational Biology, Public Library of Science, vol. 13(3), pages 1-28, March.
    6. Fabien Vinckier & Lionel Rigoux & Irma T Kurniawan & Chen Hu & Sacha Bourgeois-Gironde & Jean Daunizeau & Mathias Pessiglione, 2019. "Sour grapes and sweet victories: How actions shape preferences," PLOS Computational Biology, Public Library of Science, vol. 15(1), pages 1-24, January.

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