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Evidence for implicit and adaptive deployment of precision weighting during passive listening: a simultaneous EEG/MEG study

In the predictive coding view of perception, the context-sensitive filtering of incoming sensations is achieved through the precision weighting (PW) of sensory prediction errors (PE). We aimed at seeking empirical evidence for this context-dependent, adaptive computational process and the respective neurophysiological encoding of PW and PE during passive listening. We hypothesized that the implicit learning of contextual (second-order) regularities would exert a top-down influence onto (first-order) sensory processing. We therefore manipulated the predictability of oddball sequences. Using learning models and dynamic causal models (DCM) of effective connectivity, we reveal the trial-to-trial dynamics of perceptual learning and its implementation within a fronto-temporal network. Essentially, we show an implicit adaptation of perceptual learning to the informational content of the environment. Under predictability, it translates into a larger information integration leading to an increased learning rate (PW), associated with a decreased neuronal self-inhibition, and a decreased (precision-weighted) PE, conveyed by decreased forward connectivity. Highlights Hierarchical perceptual learning was studied with simultaneous EEG and MEG during passive listening of auditory oddball sequences. Neuro-computational modeling confirmed perceptual learning deploying within a fronto-temporal network. An implicit and computationally efficient adaptation of perceptual learning to the informational content of the sound sequence was revealed. Larger information relevance yielded a larger information integration in perceptual learning. The context-dependent tuning of sensory prediction errors was associated to cortical inhibitory mechanisms.