The design aims at identifying both the statistics that are inferred by the brain and the associated timescale of integration. The different conditions have been chosen so as to ensure differences between the theoretical surprise levels of the different learning models (i.e. learning different statistics over different timescales). For instance, in both the frequency-biased and repetition-biased conditions, one item is locally more frequent than the other, but it is true globally only in the frequency-bias condition. Learners inferring the IF based on very local or more global scales of integration will therefore markedly differ between those two conditions. Other differences between models exist in the other conditions. To quantify the diagnostic value of the design, we computed the correlation between theoretical surprise levels estimated from sequences generated randomly according to the four transition probabilities of our design (using 1000 sequences per condition). In the subplot (A), the correlation coefficients are computed after pooling together all conditions. In the subplot (B), the correlation coefficients are computed separately for each condition, and we report the smallest value. The values in brackets indicate the standard deviation across simulations. Altogether, the simulation shows that our experimental design was able to dissociate, in at least one diagnostic condition, the statistics (IF, AF or TP) and their timescale of integration (‘global’ refers to a perfect integration, ‘local’ corresponds to a decay factor ω = 6).