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Pessiglione2006
reproducing the analysis with the toolbox
PaperDopamine-dependent prediction errors underpin reward-seeking behaviour in humans. Short description of the studyI here focus on the computational modelling of behavioral data, leaving aside model-free and imaging analysis of the study In a instrumental learning task, subjects are repeatedly asked to choose among two items (binary decision) of one of 3 fixed pairs of alternatives.
The 3 pairs are equally pseudo-randomly presented to the subject. Some subjects performed the task under pharmacological conditions. Therefore three groups of subjects can be separeted
Inferences from behavioral dataHere, we won't redo the analysis from the article, but present the kinds of questions that can be adressed:
I propose an answer to these questions here. ModelsGiven the inferences we want to make, we need to build the following models
RemarksIn the model we propose, decisions for the different pairs are independent. Each pair can therefore be treated separately as different sessions of the same task. This is what we will do here, using the Extension to multiple session of the toolbox. Therefore the models we will define only deal with a single pair, which makes things easier. Note that considering multiple session doesn't necessarily mean that we consider different parameters of the model for the different sessions (this is indeed not what we want here) Code and resultsCode for the following analysis can be in the Example folder of the toolbox.
Here we will use synthetic data. We choose the parameters of our models as those reported as best fits in the article. Those fits are not reported for individual subjects. Only the mean and the 95% confidence interval are reported. We will sample parameters assuming that the distribution is gaussian and matches those.
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