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Zalta, A.

Publications and source records attributed to Zalta, A..

2 recordsLinked to original sources

Neural dynamics of predictive timing and motor engagement in music listening

Why do humans spontaneously dance to music? To test the hypothesis that motor dynamics reflect predictive timing during music listening, we built melodies with varying degrees of rhythmic predictability. Magnetoencephalography data showed that while auditory regions track the rhythm of melodies, intrinsic neural dynamics at delta (1.4 Hz) and beta (20-30 Hz) rates in the dorsal auditory pathway embody the experience of groove. Critically, neural dynamics are organized along this pathway in a spectral gradient, with the left sensorimotor cortex acting as a hub coordinating groove-related delta and beta activity. Combined with predictions of a neurodynamic model, this indicate that spontaneous motor engagement during music listening is a manifestation of predictive timing effected by interaction of neural dynamics along the dorsal auditory pathway. One-Sentence SummaryInteracting neural dynamics along the dorsal auditory pathway effect the experience of groove during music listening.

neuroscience↗

Attention to rhythms: sensory-specific constrained sampling of temporal regularities

That attention is a fundamentally rhythmic process has recently received abundant empirical evidence. The essence of temporal attention, however, is to flexibly focus in time. Whether this function is hampered by an underlying rhythmic mechanism is unknown. In six interrelated experiments, we behaviourally quantify the sampling capacities of periodic temporal attention during auditory or visual perception. We reveal the presence of limited attentional capacities, with an optimal sampling rate of ~1.4 Hz in audition and ~0.7 Hz in vision. Investigating the motor contribution to temporal attention, we show that it scales with motor rhythmic precision, maximal at ~1.7 Hz. Critically, the motor modulation is beneficial to auditory but detrimental to visual temporal attention. These results are captured by a computational model of coupled oscillators, that reveals the underlying structural constraints governing the temporal alignment between motor and attention fluctuations.

neuroscience↗