Observing the Observer - Two Parts

These have been open tabs for at least a month - I have been meaning to blog about them but time has not been my friend. So here are the abstracts, the PDF link is the end of each abstract. These are cool articles although they are also quite geeky. Here is a very brief summary of the two papers:

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.

I have to admit that portions of these articles are very challenging and that I do not get the details - I'm settling for the gist.

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

Jean Daunizeau, Hanneke E. M. den Ouden, Matthias Pessiglione, Stefan J. Kiebel, Klaas E. Stephan, Karl J. Friston

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.

Citation: Daunizeau J, den Ouden HEM, Pessiglione M, Kiebel SJ, Stephan KE, et al. (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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Observing the Observer (II): Deciding When to Decide

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.

Citation: Daunizeau J, den Ouden HEM, Pessiglione M, Kiebel SJ, Friston KJ, et al. (2010) Observing the Observer (II): Deciding When to Decide. PLoS ONE 5(12): e15555. doi:10.1371/journal.pone.0015555

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Tags: brain, decision-making, Psychology, Philosophy, cognition, Observing the Observer, I: Meta-Bayesian Models of Learning and Decision-Making, II: Deciding When to Decide, Jean Daunizeau, Hanneke E. M. den Ouden, Matthias Pessiglione, Stefan J. Kiebel, Klaas E. Stephan, Karl J. Friston, perceptual model, representation, hidden state of affairs, response model, Bayesian decision theoretic perspective, meta-Bayesian approach, PLoS ONE, posterior beliefs, prior beliefs