Multivariate analysis of brain activity patterns as a tool to understand predictive processes in speech perception

Abstract

Speech perception is heavily influenced by our expectations about what will be said. In this review, we discuss the potential of multivariate analysis as a tool to understand the neural mechanisms underlying predictive processes in speech perception. Multivariate pattern analysis might allow us to quantify the amount of information carried by the spatial or temporal patterns of brain activity ranging from the phonetic form of speech, over syllable identity to its semantic content. We suggest that using multivariate techniques to measuring informational content across the hierarchically organised speech-sensitive brain areas might enable us to specify the mechanisms by which prior knowledge and sensory speech signals are combined. Specifically, this approach might allow us to decode how different priors, e.g., about a speaker’s voice or about the topic of the current conversation, are represented at different processing stages and how thereupon the incoming speech signal is differently represented.