Neural correlates of intentional switching from ternary to binary meter in a musical hemiola pattern

摘要

Musical rhythms are often perceived and interpreted within a metrical framework that integrates timing information hierarchically based on interval ratios. Endogenous timing processes facilitate this metrical integration and allow us using the sensory context for predicting when an expected sensory event will happen (“predictive timing”). Previously, we showed that listening to metronomes and subjectively imagining the two different meters of march and waltz modulated the resulting auditory evoked responses in the temporal lobe and motor-related brain areas such as the motor cortex, basal ganglia, and cerebellum. Here we further explored the intentional transitions between the two metrical contexts, known as hemiola in the Western classical music dating back to the sixteenth century. We examined MEG from 12 musicians while they repeatedly listened to a sequence of 12 unaccented clicks with an interval of 390 ms, and tapped to them with the right hand according to a 3 + 3 + 2 + 2 + 2 hemiola accent pattern. While participants listened to the same metronome sequence and imagined the accents, their pattern of brain responses significantly changed just before the “pivot” point of metric transition from ternary to binary meter. Until 100 ms before the pivot point, brain activities were more similar to those in the simple ternary meter than those in the simple binary meter, but the pattern was reversed afterwards. A similar transition was also observed at the downbeat after the pivot. Brain areas related to the metric transition were identified from source reconstruction of the MEG using a beamformer and included auditory cortices, sensorimotor and premotor cortices, cerebellum, inferior/middle frontal gyrus, parahippocampal gyrus, inferior parietal lobule, cingulate cortex, and precuneus. The results strongly support that predictive timing processes related to auditory-motor, fronto-parietal, and medial limbic systems underlie metrical representation and its transitions.