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. 2021 Jan;58(1):e13708.
doi: 10.1111/psyp.13708. Epub 2020 Oct 27.

Medial frontal negativities predict performance improvements during motor sequence but not motor adaptation learning

Takuto Matsuhashi  1 , Sidney J Segalowitz  2 , Timothy I Murphy  2 , Yuichiro Nagano  3 , Takahiro Hirao  4 , Hiroaki Masaki  4
Affiliations

Medial frontal negativities predict performance improvements during motor sequence but not motor adaptation learning

Takuto Matsuhashi et al. Psychophysiology. 2021 Jan.

Abstract

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. Doyonet 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.

Keywords: correct-response-related negativity; cortico-striatal system; medial frontal negativity; motor learning; performance monitoring.

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Figures

FIGURE 1
FIGURE 1
Procedure of the Spatial Stroop task used in the present study. Participants were asked to respond to the pointing direction of the white arrow stimulus (i.e., up or down), but not to the arrow location. We referred to our task as a Spatial Stroop task based on Kornblum's model (1992) that can classify all types of SRC tasks in terms of overlap among relevant property of stimulus, irrelevant property of stimulus, and response dimension. According to his model, our task is classified as Type 8 that is same as classical Stroop task
FIGURE 2
FIGURE 2
Procedures for the motor sequence (1a, left) and adaptation tasks (1b, right)
FIGURE 3
FIGURE 3
Mean response time (left) and error rate (right) for the Spatial Stroop Task
FIGURE 4
FIGURE 4
Grand‐average waveforms at FCz for response‐locked (lower left) and EMG‐locked (lower right) analyses as well as topographical maps (upper left) and rectified EMG (upper right). In order to illustrate the topographies, we adopted a baseline (−30 to −10 ms before the response onset) and drew the maps based on the peak negativities (time window 0 to 55 ms for the CRN and 30 to 85 ms for the ERN)
FIGURE 5
FIGURE 5
Movement time and accuracy rates across blocks for the motor sequence and adaptation tasks
FIGURE 6
FIGURE 6
Scatterplots of performance improvement (Blocks 2 through 10) and selected ERN and CRN amplitudes
See this image and copyright information in PMC

References

    1. Abe, M. , Schambra, H. , Wassermann, E. M. , Luckenbaugh, D. , Schweighofer, N. , & Cohen, L. G. (2011). Reward improves long‐term retention of a motor memory through induction of offline memory gains. Current Biology, 21(7), 557–562. 10.1016/j.cub.2011.02.030 - DOI - PMC - PubMed
    1. Adams, J. A. (1971). A closed‐loop theory of motor learning. Journal of Motor Behavior, 3, 111–150. 10.1080/00222895.1971.10734898 - DOI - PubMed
    1. Allain, S. , Carbonnell, L. , Falkenstein, M. , Burle, B. , & Vidal, F. (2004). The modulation of the Ne‐like wave on correct responses foreshadows errors. Neuroscience Letters, 372(1–2), 161–166. 10.1016/j.neulet.2004.09.036 - DOI - PubMed
    1. Anguera, J. A. , Seidler, R. D. , & Gehring, W. J. (2009). Changes in performance monitoring during sensorimotor adaptation. Journal of Neurophysiology, 102(3), 1868–1879. 10.1152/jn.00063.2009 - DOI - PMC - PubMed
    1. Bachman, J. C. (1961). Specificity vs. generality in learning and performing two large muscle motor tasks. Research Quarterly of the American Association for Health, Physical Education and Recreation, 32(1), 3–11. 10.1080/10671188.1961.10762064 - DOI

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