Neurocomputational Underpinnings of Expected Surprise.

Predictive coding accounts of brain functions profoundly influence current approaches to perceptual synthesis. However, a fundamental paradox has emerged, that may be very relevant for understanding hallucinations, psychosis, or cognitive inflexibility: in some situations, surprise or prediction error-related responses can decrease when predicted, and yet, they can increase when we know they are predictable. This paradox is resolved by recognizing that brain responses reflect precision-weighted prediction error. This presses us to disambiguate the contributions of precision and prediction error in electrophysiology. To meet this challenge for the first time, we appeal to a methodology that couples an original experimental paradigm with fine dynamic modeling. We examined brain responses in healthy human participants ( = 20; 10 female) to unexpected and expected surprising sounds, assuming that the latter yield a smaller prediction error but much more amplified by a larger precision weight. Importantly, addressing this modulation requires the modeling of trial-by-trial variations of brain responses, that we reconstructed within a fronto-temporal network by combining EEG and MEG. Our results reveal an adaptive learning of surprise with larger integration of past (relevant) information in the context of expected surprises. Within the auditory hierarchy, this adaptation was found tied down to specific connections and reveals in particular precision encoding through neuronal excitability. Strikingly, these fine processes are automated as sound sequences were unattended. These findings directly speak to applications in psychiatry, where specifically impaired precision weighting has been suggested to be at the heart of several conditions such as schizophrenia and autism. In perception as Bayesian inference and learning, context sensitivity expresses as the precision weighting of prediction errors. A subtle mechanism that is thought to lie at the heart of several psychiatric conditions. It is thus critical to identify its neurophysiological and computational underpinnings. We revisit the passive auditory oddball paradigm by manipulating sound predictability and use a twofold modeling approach to simultaneous EEG-MEG recordings: (1) trial-by-trial modeling of cortical responses reveals a context-sensitive perceptual learning process; (2) the dynamic causal modeling (DCM) of evoked responses uncovers the associated changes in synaptic efficacy. Predictability discloses a link between precision weighting and self-inhibition of superficial pyramidal (SP) cells, a result that paves the way to a fine description of healthy and pathologic perception.