The implementation of the Bayesian model.

<p>The implementation assumes a two-dimension probability map that is updated iteratively trial by trial. It is a 200×200 matrix to code the probability of each α-β combination. Each value in the matrix is normalized such that the sum of all possibilities on the map equals 1. The pink cross denotes the current target direction and curvature. The gray dot denotes the best solution before the current trial <i>t</i> and the black dot denotes the best solution after finishing the current trial. The map from a previous trial is degraded by memory decay and it then serves as the prior before the current trial. The prediction error, the difference between the predicted reward based on the direction and curvature used in the current trial and the actual received reward, serves as likelihood distribution to update the probability map. By combining the prior and the likelihood, the probability map is updated to form the posterior distribution. The learning is demonstrated in that the best solution of the posterior, compared to that of the prior, becomes closer to the target solution. The data are from a typical trial (the 4^th trial in a 25 learning sequence) from a single participant.