Neurocomputational underpinnings of expected surprise

Abstract

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. This paradox is that 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 then presses us to disambiguate the contributions of precision and prediction error in electrophysiology. We report, for the first time, an experimental paradigm that may be able to meet this challenge. We examined brain responses 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 modelling 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 and crucially 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 it has been suggested that a specifically impaired precision weighting is at the heart of several conditions such as schizophrenia and autism.