Confidence resets reveal hierarchical adaptive learning in humans
Fig 3
Behavioral task: Learning of dynamic transition probabilities with confidence reports.
(A) Probability learning task. Human subjects (N = 23) were presented with random sequences of two stimuli, A and B. The stimuli were, in distinct blocks, either auditory or visual and they were perceived without ambiguity. At each trial, one of either stimulus was sampled according to a probability that depended on the identity of the previous stimulus: p(At|At-1) and P(Bt|Bt-1). These transition probabilities underwent occasional, abrupt changes (change points). A change point could occur at any trial with a probability that was fixed throughout the experiment. Subjects were instructed about this generative process and had to continuously estimate the (changing) transition probabilities given the observations received. Occasionally (see black dots in A), we probed their inferences by asking them, first, to report the probability of the next stimulus (i.e. report their estimate of the relevant transition probability) and second, to rate their confidence in this probability estimate. (B, C) Subjects’ responses were compared to the optimal Bayesian inference for this task. Numeric values of confidence differ between subjects and models since they are on different scales (from 0 to 1 in the former, in log-precision unit in the latter). For illustration, the optimal values were binned, the dashed line (B) denotes the identity, the plain line (C) is a linear fit, and data points correspond to subjects’ mean ± s.e.m.