Fig. 3. Model comparison results.

a Bayesian model comparison based on model fitting evidence. Subjects' predictive ratings of next trial’s pain intensity were fitted with posterior means from Bayesian models, values from Rescorla–Wagner (reinforcement learning) model, and random fixed probabilities. The winning model was the Bayesian jump frequency model, which assumes jumps in the sequence and infers the stimulus frequency. In our model comparison, the model frequency indicates how often a given model is used by participants; the model exceedance probability measures how likely it is that any given model is more frequent than the other models, and the protected exceedance probability is the corrected exceedance probability for observations due to chance. b Individual subject model evidence (each row represents a subject; colorbar indicates the model probability ranging from 0 to 1). Source data are provided as a Source Data file.