Principal component decomposition of acoustic and neural representations of time-varying pitch reveals adaptive efficient coding of speech covariation patterns

Abstract

Understanding the effects of statistical regularities on speech processing is a central issue in auditory neuroscience. To investigate the effects of distributional covariance on the neural processing of speech features, we introduce and validate a novel approach: decomposition of time-varying signals into patterns of covariation extracted with Principal Component Analysis. We used this decomposition to assay the sensory representation of pitch covariation patterns in native Chinese listeners and non-native learners of Mandarin Chinese tones. Sensory representations were examined using the frequency-following response, a far-field potential that reflects phase-locked activity from neural ensembles along the auditory pathway. We found a more efficient representation of the covariation patterns that accounted for more redundancy in the form of distributional covariance. Notably, long-term language and short-term training experiences enhanced the sensory representation of these covariation patterns.