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Medial frontal negativities predict performance improvements during motor sequence but not motor adaptation learning
Author(s) -
Matsuhashi Takuto,
Segalowitz Sidney J.,
Murphy Timothy I.,
Nagano Yuichiro,
Hirao Takahiro,
Masaki Hiroaki
Publication year - 2021
Publication title -
psychophysiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.661
H-Index - 156
eISSN - 1469-8986
pISSN - 0048-5772
DOI - 10.1111/psyp.13708
Subject(s) - psychology , motor learning , stroop effect , cognitive psychology , neuroscience , motor skill , task (project management) , supplementary motor area , adaptation (eye) , error related negativity , anterior cingulate cortex , sequence learning , cognition , functional magnetic resonance imaging , management , economics
Alterations in our environment require us to learn or alter motor skills to remain efficient. Also, damage or injury may require the relearning of motor skills. Two types have been identified: movement adaptation and motor sequence learning. Doyon et al. (2003, Distinct contribution of the cortico‐striatal and cortico‐cerebellar systems to motor skill learning. Neuropsychologia, 41(3), 252‐262) proposed a model to explain the neural mechanisms related to adaptation (cortico‐cerebellar) and motor sequence learning (cortico‐striatum) tasks. We hypothesized that medial frontal negativities (MFNs), event‐related electrocortical responses including the error‐related negativity (ERN) and correct‐response‐related negativity (CRN), would be trait biomarkers for skill in motor sequence learning due to their relationship with striatal neural generators in a network involving the anterior cingulate and possibly the supplementary motor area. We examined 36 participants' improvement in a motor adaptation and a motor sequence learning task and measured MFNs elicited in a separate Spatial Stroop (conflict) task. We found both ERN and CRN strongly predicted performance improvement in the sequential motor task but not in the adaptation task, supporting this aspect of the Doyon model. Interestingly, the CRN accounted for additional unique variance over the variance shared with the ERN suggesting an expansion of the model.