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Tutorial 3: Fitting a Causal Inference Model to Behavioral Data

Difficulty level

This tutorial covers computing all the necessary steps to perform model inversion (estimate the model parameters such as 𝑝𝑐𝑜𝑚𝑚𝑜𝑛 that generated data similar to that of a participant). We will describe all the steps of the generative model first, and in the last exercise we will use all these steps to estimate the parameter 𝑝𝑐𝑜𝑚𝑚𝑜𝑛 of a single participant using simulated data. The generative model will be the same Bayesian model we have been using throughout tutorial 2: a mixture of Gaussian prior (common + independent priors) and a Gaussian likelihood.

Topics covered in this lesson
  • Likelihood array
  • Causal mixture of Gaussian priors
  • Bayes' rule and posterior array
  • Binary decision matrix
  • Probabilities of encoded stimuli
  • Normalization and expected estimate distribution
  • Model fitting

Experience with Python Programming Language

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