In a changing world, learning must be adaptive. When a change point occurs, what has been learned previously suddenly becomes outdated and must quickly be re-learned on the basis of new data. By contrast, in stable periods, we should accumulate more data in order to stabilize and refine what has been learned previously. This ability to balance flexibility and stability appropriately is the core problem of adaptive learning. In practice, this problem is very difficult because our world is also uncertain, such that it is not clear whether flexibility or stability should be favored. For instance, a heatwave in winter may denote an infrequent fluctuation in normal weather or a profound change in climate. Should I trust my current knowledge and stabilize it, or be flexible and revise it? Bayesian inference is a mathematical tool that affords optimal solutions to arbitrate between flexibility and stability. Inspired by those Bayes-optimal solutions, I propose that human algorithms for adaptive learning are accurate because they rely on a sense of confidence to strike the balance between flexibility and stability. This project now aims to uncover the neuro-cheminal mechanisms of this confidence-weighted learning algorithm. I propose that the shaping of brain-scale dynamic interactions by noradrenaline may implement a confidence-weighting of information during learning. To test this idea, I propose to use an integrated application of computational modeling, behavioral data, functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG) and pharmacological interventions in human subjects. The project is organized into three work-packages (WPs) that are unified by the use of a single probabilistic learning task and an optimal Bayesian model that were previously validated. In WP1, I will improve fMRI methods at high field (7T) to probe non-invasively the activity of the locus-coeruleus (LC, the main noradrenergic nucleus) in humans. Preliminary data in a 3T scanner are already promising. With this method, I will then relate on a trial-by-trial basis, the activity in the LC to behavior and to the hidden variables of Bayes-optimal inference during a learning task, and therefore test the hypothesis that LC activity correlates with confidence. Such correlations would be compatible with the hypothesis that LC activity mediates an effect of confidence on learning. In WP2, I will test the causal role of noradrenaline in the confidence-weighting of learning, and its specificity with respect to another neuromodulator (acetylcholine) using a pharmacological manipulation. This manipulation will be combined with MEG so as to reveal the potential mediation of LC activity onto brain-dynamics in confidence-weighted computations. My hypothesis is that LC activity regulates learning by modulating information flow in the cortex, which can be assessed with frequency fingerprints in MEG signals. In WP3, I will probe the neural codes of the probability distributions inferred during the task, and the format of the associated confidence information which is critical for adaptive learning. I combine artificial neural networks and computational neuroscience to propose several putative codes. I will develop encoding models, which allow to translate the neural details of those putative codes into testable predictions at the fMRI level. I will use machine learning techniques to test and compare those models. Together, those results may advance neuroscience and learning theories, which is the core aim of this project. The project also offers speculative, but promising implications for technological and medical developments.