Confidence resets reveal hierarchical adaptive learning in humans
Fig 2
Correlation between the hierarchical and flat models in a classic probability learning task is higher for probability estimates than for confidence levels.
We simulated a classic probability learning task, similar to the one by Behrens et al 2007. In this task, the binary observation made on each trial (e.g. presence or absence of reward) is governed by a probability that changes discontinuously at so-called change points. For the sake of generality, we varied the volatility (probability of a change point) and the step size of those changes (minimum fold change, in odds ratio, affecting the generative probability). For each combination of volatility and step size, we simulated 100 sequences to achieve stable results and we fit the single free parameter of each model (respectively, a priori volatility and leak factor) onto the actual generative probabilities of the observed stimuli in the sequences. The resulting parameterized models therefore return their best possible estimate of the hidden regularities, in each volatility-step size condition. We then simulated new sequences (again, 100 per condition) to measure (A) the correlation between the estimated probabilities of stimuli between the two models, and (B) the correlation (Pearson’s rho) between the confidence (log-precision) that those models entertained in those estimates. The correlations indicate that probability estimates are nearly indistinguishable between the two models, whereas their confidence levels are more different.