A quantitative model reveals a frequency ordering of prediction and prediction-error signals in the human brain.

ABSTRACT: The human brain is proposed to harbor a hierarchical predictive coding neuronal network underlying perception, cognition, and action. In support of this theory, feedforward signals for prediction error have been reported. However, the identification of feedback prediction signals has been elusive due to their causal entanglement with prediction-error signals. Here, we use a quantitative model to decompose these signals in electroencephalography during an auditory task, and identify their spatio-spectral-temporal signatures across two functional hierarchies. Two prediction signals are identified in the period prior to the sensory input: a low-level signal representing the tone-to-tone transition in the high beta frequency band, and a high-level signal for the multi-tone sequence structure i

SUBMITTER: Chao ZC 

PROVIDER: S-EPMC9550773 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature