Impact of reinforcement on action selection, initiation and execution during motor skill learning
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1
Institute of NeuroScience, Université Catholique de Louvain, Belgium
The ability to learn motor skills is a fundamental feature of human behavior, which relies both on sensory and on reinforcement feedbacks (i.e., reward and punishment, (Izawa and Shadmehr, 2011; Galea et al., 2015; Vassiliadis et al., 2019). How such reinforcements lead to improved performance during motor skill learning remains an open question. In fact, skill learning can result from improvements at several levels of control, including action selection, initiation or execution. Here, we tested the impact of reinforcement on these different levels during motor skill learning (Chen et al., 2017)
After 2 short practice blocks, subjects performed 10 blocks (360 trials in total) of a modified version of a force-tracking task (Abe et al., 2011; Steel et al., 2016, 2019). Each trial started with a cursor appearing at the bottom of the screen and subjects were asked to squeeze a pinch-grip sensor to bring the cursor at the center of a fixed target and maintain it there for the rest of the trial. To reach the target, subjects had to exert a force (TargetForce) corresponding to 10 % of the individual maximal voluntary contraction. On most trials, the cursor disappeared shortly after the beginning of the trial. Hence, subjects had to learn to approximate the TargetForce in the absence of visual cursor. A trial was classified as successful if the mean of the difference between the actual force and the TargetForce was under an individualized threshold. At the end of each trial, subjects received a reinforcement feedback based on their performance (i.e., Success or Failure). In this task, success depended on force control at the level of action selection (i.e., ForceSel; the closer the mean of the selected force was to TargetForce, the higher the chances of success), at the level of initiation (i.e., the faster the onset [ForceStart] and the steeper the rate [ForceRate] of force production, the better) and execution (i.e., ForceExe; the lower the force variability during the tonic phase, the better). Moreover, we analyzed the evolution of the different force variables at the three levels of control, as well as the impact of reinforcement (i.e., Success or Failure) on performance in the next trial across training.
As expected, the proportion of successful trials increased over training, indicating that subjects learned the motor skill. Moreover, we found that skill learning occurred at the level of action selection (ForceSel closer to TargetForce), initiation (earlier ForceStart and steeper ForceRate) and execution (reduced ForceExe). Interestingly, subjects improved at all levels of control in trials following a Failure, while they exhibited the opposite pattern following a Success. However, importantly, this effect of reinforcement changed over the course of learning. In fact, the beneficial effect of Failure increased across training while the detrimental effect of Success decreased. It remains to be determined whether these effects would vary with a reinforcement involving an actual monetary loss or gain.
Acknowledgements
PV was a PhD student supported by the Fund for Research Training in Industry and Agriculture (FRIA). GD was a post-doctoral fellow supported by the Belgian FNRS. FC was supported by the Belgian FNRS. JD was supported by grants from the FSR of the Université Catholique de Louvain, and the Belgian FNRS.
References
Abe M, Schambra H, Wassermann EM, Luckenbaugh D, Schweighofer N, Cohen LG (2011) Reward improves long-term retention of a motor memory through induction of offline memory gains. Curr Biol 21:557–562.
Chen X, Holland P, Galea JM (2017) The effects of reward and punishment on motor skill learning. Curr Opin Behav Sci 20:83–88 Available at: http://dx.doi.org/10.1016/j.cobeha.2017.11.011.
Galea JM, Mallia E, Rothwell J, Diedrichsen J (2015) The dissociable effects of punishment and reward on motor learning. Nat Neurosci 18:597–602 Available at: http://dx.doi.org/10.1038/nn.3956%5Cn10.1038/nn.3956%5Cnhttp://www.nature.com/neuro/journal/v18/n4/abs/nn.3956.html#supplementary-information.
Izawa J, Shadmehr R (2011) Learning from sensory and reward prediction errors during motor adaptation. PLoS Comput Biol 7:1–12.
Steel A, Silson EH, Stagg CJ, Baker CI (2016) The impact of reward and punishment on skill learning depends on task demands. Sci Rep 6:1–9 Available at: http://dx.doi.org/10.1038/srep36056.
Steel A, Silson EH, Stagg CJ, Baker CI (2019) Differential impact of reward and punishment on functional connectivity after skill learning. Neuroimage 189:95–105 Available at: https://doi.org/10.1016/j.neuroimage.2019.01.009.
Vassiliadis P, Derosiere G, Duque J (2019) Beyond Motor Noise : Considering Other Causes of Impaired Reinforcement Learning in Cerebellar Patients. Eneuro 6:1–4.
Keywords:
Reinforcemenet learning,
action selection,
action execution,
Action initiation,
Reward,
motor skill learning
Conference:
13th National Congress of the Belgian Society for Neuroscience , Brussels, Belgium, 24 May - 24 May, 2019.
Presentation Type:
Poster presentation
Topic:
Behavioral/Systems Neuroscience
Citation:
Vassiliadis
P,
Derosiere
G,
Dubuc
C,
Crevecoeur
F and
Duque
J
(2019). Impact of reinforcement on action selection, initiation and execution during motor skill learning.
Front. Neurosci.
Conference Abstract:
13th National Congress of the Belgian Society for Neuroscience .
doi: 10.3389/conf.fnins.2019.96.00084
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Received:
25 Mar 2019;
Published Online:
27 Sep 2019.
*
Correspondence:
Mr. Pierre Vassiliadis, Institute of NeuroScience, Université Catholique de Louvain, Louvain-la-Neuve, Walloon Brabant, 1200 Brussels, Belgium, pierre.vassiliadis@gmail.com