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Updated: Jun 4, 2026

The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task
Published on: May 3, 2018
Functional neuroanatomy associated with the expression of distinct movement kinematics in motor sequence learning
1Functional Neuroimaging Unit, Geriatric Institute Research Center, Université de Montreal, Queen Mary 4565, Montreal, Canada H3W 1W5. pierre.orban@criugm.qc.ca
Abstract:
A broad range of motor skills, such as speech and writing, evolves with the ability to articulate elementary motor movements into novel sequences that come to be performed smoothly through practice. Neuroimaging studies in humans have demonstrated the involvement of the cerebello-cortical and striato-cortical motor loops in the course of motor sequence learning. Nonetheless, the nature of the improvement and brain mechanisms underlying different parameters of movement kinematics are not yet fully ascertained. We aimed at dissociating the cerebral substrates related to the increase in performance on two kinematic indices: velocity, that is the speed with which each single movement in the sequence is produced, and transitions, that is the duration of the gap between these individual movements. In this event-related fMRI experiment, participants practiced an eight-element sequence of finger presses on a keypad which allowed to record those kinematic movement parameters. Velocity was associated with activations in the ipsilateral spinocerebellum (lobules 4-5, 8 and medial lobule 6) and in the contralateral primary motor cortex. Transitions were associated with increased activity in the neocerebellum (lobules 6 bilaterally and lobule 4-5 ipsilaterally), as well as with activations within the right and left putamen and a broader bilateral network of motor cortical areas. These findings indicate that, rather than being the product of a single mechanism, the general improvement in motor performance associated with early motor sequence learning arises from at least two distinct kinematic processes, whose behavioral expressions are supported by partially overlapping and segregated brain networks.
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