Confidence resets reveal hierarchical adaptive learning in humans
Fig 4
A qualitative signature of hierarchical learning in confidence reports.
(A) Divergent predictions of hierarchical versus flat learning models. Two fragments of sequences are shown in which one stimulus (‘A’) is consecutively repeated 10 times. In the upper fragment, this streak of repetitions is highly unlikely (or ‘suspicious’) given the context, and may indicate that the underlying statistics changed. By contrast, in the lower fragment, the same streak is not unlikely, and does not suggest a change point. The heat maps show the posterior probability distribution of P(B|B), i.e. the probability of a repetition of the other stimulus (B), estimated by the hierarchical and flat models. In a hierarchical model, unlikely streaks arouse the suspicion of a global change in statistics, causing the model to become uncertain about its estimates of both transition probabilities, despite having acquired no direct evidence on P(B|B). In a flat model, by contrast, a suspicious streak of As will not similarly decrease the confidence in P(B|B), because a flat model does not track global change points. To test for this effect, pre/post questions (indicated by a star) were placed immediately before and after selected streaks, to obtain subjective estimates of the transition probability corresponding to the stimulus not observed during the streak. Streaks were categorized as suspicious if they aroused the suspicion of a change point from the hierarchical, Bayes-optimal viewpoint. Note that the flat model also shows a decrease in confidence, because it progressively forgets its estimates about P(B|B) during a streak of As, but, there is no difference between suspicious and non-suspicious streaks. (B) For the sequences presented to subjects, the change in confidence (post-streak minus pre-streak) was significantly modulated by streak type in the hierarchical model, but not in a flat model. (C) Subjects’ confidence showed an effect of streak type predicted by the optimal hierarchical model. As in Fig 4C, confidence values in subjects and models are on different scales. Error bars correspond to the inter-subject quartiles, distributions show subjects' data; significance levels correspond to paired t-test with p<0.005 (**) and p<10–12 (***).