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Observing the Observer (I): Meta-Bayesian Models of Learning and Decision-Making

Daunizeau, J; den Ouden, HEM; Pessiglione, M; Kiebel, SJ; Stephan, KE; Friston, KJ; (2010) Observing the Observer (I): Meta-Bayesian Models of Learning and Decision-Making. PLOS ONE , 5 (12) , Article e15554. 10.1371/journal.pone.0015554. Green open access

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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.

Type: Article
Title: Observing the Observer (I): Meta-Bayesian Models of Learning and Decision-Making
Open access status: An open access version is available from UCL Discovery
DOI: 10.1371/journal.pone.0015554
Publisher version: http://dx.doi.org/10.1371/journal.pone.0015554
Language: English
Additional information: © 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. This work was funded by the Wellcome Trust (HDO, KJF), SystemsX.ch (JD, KES) and NCCR “Neural Plasticity” (KES). The authors also gratefully acknowledge support by the University Research Priority Program “Foundations of Human Social Behaviour” at the University of Zurich (JD, KES). Relevant URLs are given below: SystemsX.ch: http://www.systemsx.ch/projects/systemsx​ch-projects/research-technology-and-deve​lopment-projects-rtd/neurochoice/; NCCR: “Neural Plasticity”: http://www.nccr-neuro.ethz.ch/; University Research Priority Program “Foundations of Human Social Behaviour” at the University of Zurich: http://www.socialbehavior.uzh.ch/index.h​tml. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Keywords: SACCADIC EYE-MOVEMENTS, VISUAL-CORTEX, FREE-ENERGY, INFERENCE, UNCERTAINTY, PERCEPTION, COMPUTATIONS, INFORMATION, HUMANS, MOTION
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
URI: https://discovery.ucl.ac.uk/id/eprint/1300091
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