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Updated: Jun 26, 2026

A Method for Evaluating Timeliness and Accuracy of Volitional Motor Responses to Vibrotactile Stimuli
Published on: August 2, 2016
Prediction-based sensory attenuation is related to prediction-based motor attenuation
Dominic M D Tran1, Nicolas A McNair1, Alexis E Whitton2
1The University of Sydney, Camperdown, NSW, Australia.
None:
When sensory inputs can be predicted by an organism's own actions or external environmental cues, neural activity is often attenuated compared with sensory inputs that are unpredictable. We have recently demonstrated that attenuation to predictable inputs is also observed when stimulating the motor system with transcranial magnetic stimulation (TMS). Akin to sensory attenuation, the motor system is less responsive to predictable TMS compared with unpredicted TMS. However, it remains unclear whether these motor prediction signals are related to, or even dependent on, sensory prediction. Using a two-coil single-pulse TMS setup to target distinct brain regions, we arranged different warning cues to predict different regions of stimulation and measured motor attenuation using motor-evoked potentials. We found that expecting TMS over the motor cortex produced stronger attenuation than expecting TMS over a non-motor region, confirming that the attenuation observed is directly linked to activation of the motor system and not due to the sensory by-products of TMS. Using combined TMS-EEG, we measured motor attenuation with motor-evoked potentials, and simultaneously measured sensory attenuation to the sound of TMS (a coil "click") with auditory-evoked potentials. We found that both motor and auditory potentials were attenuated to predictable TMS compared with unpredictable TMS. Critically, the magnitude of auditory attenuation predicted the magnitude of motor attenuation. Our results reveal a close correspondence between prediction processing in the sensory and motor systems. The findings provide evidence consistent with predictive coding being governed by domain-general properties across distinct neural systems, suggesting there could be common mechanisms responsible for different forms of predictive learning.
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