Fig. S7.

Adaptation of the sampling policy as a function of the sampled evidence. Previous research on category learning has shown evidence for enhanced attention near the category boundary (32). One key difference is that in this line of research, the category boundary is unknown to the participant and must be learned by sampling information. By contrast, in our task, participants were fully aware of the bound (i.e., the reference value on each block), and no further learning about the bound occurs during sampling, because feedback was only administered at the end of the trial, after participants had discontinued sampling. When the boundary is unknown, it is optimal to sample items that fall close to the current estimate of its location, to gather the most information (i.e., reduce uncertainty) about its precise location. Adaptive gain theory has shown evidence for up-weighting of decision information that is consistent with the overall information shown thus far (40). To investigate whether participants adapt their sampling policy as a function of the sampled evidence, we again characterized discrete fixations larger than 100 ms using Eyelink preset criteria. Next, we quantified the information value of each fixation based on each fixation’s coordinates on the TE landscape. By comparing the sign of the information value of each fixation, we could check whether each fixation confirmed (i.e., same sign of the information value; =1) or disconfirmed (i.e., opposite sign; = –1) their current hypothesis (i.e., confirmation, defined as the running mean of the fixations so far). After calculating the average of these values per trial, this yields a quantitative measure of whether participants adapted their sampling policy as a function of the sampled evidence on each trial. Next, we constructed a null-distribution specific to our experiment by randomly permuting the numbers of each trial and repeating the analysis 1,000 times, to carefully control for any possible correlations in the values. Thus, each fixation had a different TE, whereas the information sampling characteristics remained the same. We then compared the average confirmation of each fixation (sorted by position in which they occurred) in our experiment to the one found under this null distribution by calculating the average difference score between them for each fixation (positive scores indicating confirmation, negative scores indicating disconfirmation, and zero indicating sampling independent of the previously sampled information). Critically, this figure shows significant evidence for confirming evidence seeking—that is, a tendency for humans to sample information that confirms the running hypothesis up to but not including that sample. This was true for each of the fixations made from the third to the ninth (all P < 0.05, d = 0.43). In other words, participants view by preference information that confirms their running hypothesis over all samples viewed thus far. *P = 0.021; **P = 0.006; ***P < 0.001. Bars are shown with 95% confidence intervals. This suggests that participants are indeed adapting their sampling policy as new information arrives with each successive fixation, consistent with an adjustment of the sampling policy toward the mean of the trial, rather than to the reference alone.