Learning in a changing and uncertain world is difficult. In this context, facing a discrepancy between my current belief and new observations may reflect random fluctuations (e.g. my commute train is unexpectedly late, but it happens sometimes), if so, I should ignore this discrepancy and not change erratically my belief. However, this discrepancy could also denote a profound change (e.g. the train company changed and is less reliable), in this case, I should promptly revise my current belief. Human learning is adaptive: we change how much we learn from new observations, in particular, we promote flexibility when facing profound changes. A mathematical analysis of the problem shows that we should increase flexibility when the confidence about our current belief is low, which occurs when a change is suspected. Here, I show that human learners entertain rational confidence levels during the learning of changing probabilities. This confidence modulates intrinsic properties of the brain state (oscillatory activity and neuromodulation) which in turn amplifies or reduces, depending on whether confidence is low or high, the neural responses to discrepant observations. This confidence-weighting mechanism could underpin adaptive learning.
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