Figure A1.

Results of a simulated estimation of model parameters. In this example, eight games were simulated and the outcomes (and choices) were used to evaluate the decision probability under different values of prior precision. Upper left: the observed distribution of latencies over the eight games. Upper right: the log likelihood of obtaining the choice behaviour under different levels of the prior precision (from 2 to 16), with one function for each games. Lower left: because the choices from the eight games were conditionally independent, the total log likelihood is the sum of log likelihoods from each game. Under the (Laplace) assumption that the posterior distribution over prior precision is Gaussian, one can use the log likelihood to estimate the posterior expectation and precision of the model parameter. In this case, the scale parameter of the priors over precision had a true value of eight (vertical line).