Inter-trial effects in priming of pop-out: Comparison of computational updating models
Fig 12
Example of response-based updating.
Example of the starting point changes associated with the different positions predicted by the gradient-like dependence on the change of position (i.e., “PG Bayesian S0” updating rule) for the first eight trials of a typical participant (A), and the starting point for the target position on the same eight trials (B). The position of the triangles represent the target position on each trial, and the shape of the triangle indicates the response-critical feature (RCF, i.e., whether the notch was on the top or bottom of the target item).