According to the predictive coding model of perception, the brain constantly generates predictions of the upcoming sensory inputs. Perception is realised through a hierarchical generative model which aims at minimising the discrepancy between predictions and the incoming sensory inputs (i.e., prediction errors). Notably, prediction errors are weighted depending on precision of prior information. However, it remains unclear whether and how the brain monitors prior precision when minimising prediction errors in different contexts. The current study used magnetoencephalography (MEG) to address this question. We presented participants with repetition of two non-predicted probes embedded in context of high and low precision, namely mispredicted and unpredicted probes. Non-parametric permutation statistics showed that the minimisation of precision-weighted prediction errors started to dissociate on early components of the auditory responses (including the P1m and N1m), indicating that the brain can differentiate between these scenarios at an early stage of the auditory processing stream. Permutation statistics conducted on the depth-weighted statistical parametric maps (dSPM) source solutions of the repetition difference waves between the two non-predicted probes further revealed a cluster extending from the frontal areas to the posterior temporal areas in the left hemisphere. Overall, the results suggested that context precision not only changes the weighting of prediction errors but also modulates the dynamics of how prediction errors are minimised upon the learning of statistical regularities (achieved by stimulus repetition), which likely involves differential activation at temporal-frontal regions.
ASJC Scopus subject areas
- Cognitive Neuroscience