How musical rhythms entrain the human brain : clarifying the neural mechanisms of sensory-motor entrainment to rhythms

Abstract

When listening to music, people across cultures tend to spontaneously perceive and move the body along a periodic pulse-like meter. Increasing evidence suggests that this ability is supported by neural mechanisms that selectively amplify periodicities corresponding to the perceived metric pulses. However, the nature of these neural mechanisms, i.e., the endogenous or exogenous factors that may selectively enhance meter periodicities in brain responses to rhythm, remains largely unknown. This question was investigated in a series of studies in which the electroencephalogram (EEG) of healthy participants was recorded while they listened to musical rhythm. From this EEG, selective contrast at meter periodicities in the elicited neural activity was captured using frequency-tagging, a method allowing direct comparison of this contrast between the sensory input, EEG response, biologically-plausible models of auditory subcortical processing, and behavioral output. The results show that the selective amplification of meter periodicities is shaped by a continuously updated combination of factors including sound spectral content, long-term training and recent context, irrespective of attentional focus and beyond auditory subcortical nonlinear processing. Together, these observations demonstrate that perception of rhythm involves a number of processes that transform the sensory input via fixed low-level nonlinearities, but also through flexible mappings shaped by prior experience at different timescales. These higher-level neural mechanisms could represent a neurobiological basis for the remarkable flexibility and stability of meter perception relative to the acoustic input, which is commonly observed within and across individuals. Fundamentally, the current results add to the evidence that evolution has endowed the human brain with an extraordinary capacity to organize, transform, and interact with rhythmic signals, to achieve adaptive behavior in a complex dynamic environment.

Date of Award	2020
Original language	English