Value Based Decision Making

To efficiently solve every day tasks it is necessary to represent the “state-space” of the decision problem. To assign values to the stimuli and make appropriate actions, we first require a representation of those stimuli, then the understanding of the context in which those stimuli are presented and of the rules which govern the evolution of decision problem, and finally we need to identify the relevance of available actions.

Here we compare various perceptual and response models [1] that capture different aspects of the experimental tasks to which humans subjects were exposed. The model comparison then returns the pair of perceptual and response models which is most likely to explain the behavior of experimental subjects [2].

[1] Daunizeau, Jean, et al. "Observing the observer (I): meta-Bayesian models of learning and decision-making." PLoS One 5.12 (2010): e15554.
[2] Stephan, Klaas Enno, et al. "Bayesian model selection for group studies." Neuroimage 46.4 (2009): 1004-1017.