Behavioural analysis
We first fitted the HGF model to the sequence of choices from each participant for each session to estimate the individual trajectories of cue–outcome association learning. Thus, we recovered three critical covert computational quantities reflecting the learning process: the sensory prediction error, the adaptive learning rate and, as a product, the adaptive prediction error. The sensory prediction error constitutes the discrepancy between the correctness of the participant’s choice and the expectation of this choice being correct. The learning rate weights the sensory prediction error by considering the current subjective uncertainty about the cue–outcome association and thereby modulates its impact on learning; note the learning rate is supposed to fluctuate with the volatility of the task structure and is therefore adaptive