Individual Solutions in Motor Learning: Combining Different Analyses

Click name to view affiliation

Vitor Leandro da Silva Profeta Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil

Search for other papers by Vitor Leandro da Silva Profeta in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0003-4920-7822 *
and
Claisyellen Silva Campos Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil

Search for other papers by Claisyellen Silva Campos in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0009-0005-1547-5134
Restricted access

Different individuals learn different solutions to the same perceptual-motor task regardless of the fact that they may undergo the same practice conditions. In the current study, we characterized individual solutions to a perceptual-motor task. Eighteen self-declared right-handed participants were requested to intercept a moving target controlling a virtual ball using a computer mouse. Target speed varied across trials. Participants visited the lab 2 days in a row. They practiced 250 trials on Day 1 and 50 trials on Day 2. We assessed participants’ preferred speed and maximum speed on both days. We combined a qualitative description of solutions on the task space and the quantitative growth curve analysis to address individual differences. Results indicated an overall trend to increase the ball release speed to handle the task constraints. Moreover, the local shape of the solution manifold constrained individuals’ solutions. Contrary to our expectations, neither individual preferred speed nor individual maximum speed improved model fit.

  • Collapse
  • Expand
  • Ackerman, P.L. (1990). A correlational analysis of skill specificity: Learning, abilities, and individual differences. Journal of Experimental Psychology: Learning, Memory, and Cognition, 16(5), 883901. https://doi.org/10.1037/0278-7393.16.5.883

    • Search Google Scholar
    • Export Citation
  • Anderson, D.I., Lohse, K.R., Lopes, T.C.V., & Williams, A.M. (2021). Individual differences in motor skill learning: Past, present and future. Human Movement Science, 78(May), Article 102818. https://doi.org/10.1016/j.humov.2021.102818

    • Search Google Scholar
    • Export Citation
  • Anguera, J.A., Reuter-Lorenz, P.A., Willingham, D.T., & Seidler, R.D. (2010). Contributions of spatial working memory to visuomotor learning. Journal of Cognitive Neuroscience, 22(9), 19171930. https://doi.org/10.1162/jocn.2009.21351

    • Search Google Scholar
    • Export Citation
  • Bates, D., Mächler, M., Bolker, B.M., & Walker, S.C. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 148. https://doi.org/10.18637/jss.v067.i01

    • Search Google Scholar
    • Export Citation
  • Caljouw, S.R., van der Kamp, J., & Savelsbergh, G.J.P. (2005). Bi-phasic hitting with constraints on impact velocity and temporal precision. Human Movement Science, 24(2), 206217. https://doi.org/10.1016/j.humov.2005.04.003

    • Search Google Scholar
    • Export Citation
  • Caljouw, S.R., van der Kamp, J., & Savelsbergh, G.J.P. (2006). The impact of task-constraints on the planning and control of interceptive hitting movements. Neuroscience Letters, 392(1–2), 8489. https://doi.org/10.1016/j.neulet.2005.08.067

    • Search Google Scholar
    • Export Citation
  • Christina, R.W. (1997). Concerns and issues in studying and assessing motor learning. Measurement in Physical Education and Exercise Science, 1(1), 1938. https://doi.org/10.1207/s15327841mpee0101_2

    • Search Google Scholar
    • Export Citation
  • Fajen, B.R. (2005). The scaling of information to action in visually guided braking. Journal of Experimental Psychology: Human Perception and Performance, 31(5), 11071123. https://doi.org/10.1037/0096-1523.31.5.1107

    • Search Google Scholar
    • Export Citation
  • Golenia, L., Schoemaker, M.M., Mouton, L.J., & Bongers, R.M. (2014). Individual differences in learning a novel discrete motor task. PLoS One, 9(11), Article e0112806. https://doi.org/10.1371/journal.pone.0112806

    • Search Google Scholar
    • Export Citation
  • Green, P., & Macleod, C.J. (2016). SIMR: An R package for power analysis of generalized linear mixed models by simulation. Methods in Ecology and Evolution, 7(4), 493498. https://doi.org/10.1111/2041-210X.12504

    • Search Google Scholar
    • Export Citation
  • Harris, C.K., & Wolpert, D.M. (1998). Signal-dependent noise determines motor planning. Nature, 394, 780784. https://doi.org/https://doi.org/10.1038/29528

    • Search Google Scholar
    • Export Citation
  • Harrison, H.S., Turvey, M.T., & Frank, T.D. (2016). Affordance-based perception-action dynamics: A model of visually guided braking. Psychological Review, 123(3), 305323. https://doi.org/10.1037/rev0000029

    • Search Google Scholar
    • Export Citation
  • Hoenig, J.M., & Heisey, D.M. (2001). The abuse of power: The pervasive fallacy of power calculations for data analysis. The American Statistician, 55(1), 1924. https://doi.org/10.1198/000313001300339897

    • Search Google Scholar
    • Export Citation
  • Hossner, E., Zahno, S., & Anderson, N. (2022). Beyond task-space exploration: On the role of variance for motor control and learning. Frontiers in Physiology, 13, Article 935273. https://doi.org/10.3389/fpsyg.2022.935273

    • Search Google Scholar
    • Export Citation
  • Jacobs, D.M., & Michaels, C.R. (2007). Direct learning. Ecological Psychology, 19(4), 321349. https://doi.org/10.1080/10407410701432337

    • Search Google Scholar
    • Export Citation
  • King, A.C., Ranganathan, R., & Newell, K.M. (2012). Individual differences in the exploration of a redundant space-time motor task. Neuroscience Letters, 529(2), 144149. https://doi.org/10.1016/j.neulet.2012.08.014

    • Search Google Scholar
    • Export Citation
  • Lohse, K.R. (2020). Methodological advances in motor learning and development. Journal of Motor Learning and Development, 8(1), 113. https://doi.org/10.1123/jmld.2019-0054

    • Search Google Scholar
    • Export Citation
  • Magill, R., & Anderson, D.I. (2015). Motor learning and control: Concepts and applications. McGraw Hill.

  • Michaels, C.F., Gomes, T.V.B., & Benda, R.N. (2017). A direct-learning approach to acquiring a bimanual tapping skill. Journal of Motor Behavior, 49(5), 550567. https://doi.org/10.1080/00222895.2016.1247031

    • Search Google Scholar
    • Export Citation
  • Mirman, D., Dixon, J.A., & Magnuson, J.S. (2008). Statistical and computational models of the visual world paradigm: Growth curves and individual differences. Journal of Memory and Language, 59(4), 475494. https://doi.org/10.1016/j.jml.2007.11.006

    • Search Google Scholar
    • Export Citation
  • Mirman, D. (2014). Growth curve analysis and visualization using R. CRC Press.

  • Müller, H., & Sternad, D. (2004). Decomposition of variability in the execution of goal-oriented tasks: Three components of skill improvement. Journal of Experimental Psychology: Human Perception and Performance, 30(1), 212233. https://doi.org/10.1037/0096-1523.30.1.212

    • Search Google Scholar
    • Export Citation
  • Müller, S., Gurisik, Y., Hecimovich, M., Harbaugh, A.G., & Vallence, A.M. (2017). Individual differences in short-term anticipation training for high-speed interceptive skill. Journal of Motor Learning and Development, 5(1), 160176. https://doi.org/10.1123/jmld.2016-0029

    • Search Google Scholar
    • Export Citation
  • Newell, K.M. (1989). On task and theory specificity. Journal of Motor Behavior, 21(1), 9296. https://doi.org/10.1080/00222895.1989.10735467

    • Search Google Scholar
    • Export Citation
  • Pacheco, M.M., Lafe, C.W., & Newell, K.M. (2019). Search strategies in the perceptual-motor workspace and the acquisition of coordination, control, and skill. Frontiers in Psychology, 10(AUG), Article 01874. https://doi.org/10.3389/fpsyg.2019.01874

    • Search Google Scholar
    • Export Citation
  • Pacheco, M.M., & Newell, K.M. (2018). Learning a specific, individual and generalizable coordination function: Evaluating the variability of practice hypothesis in motor learning. Experimental Brain Research, 236(12), 33073318. https://doi.org/10.1007/s00221-018-5383-3

    • Search Google Scholar
    • Export Citation
  • Parma, J.O., Profeta, V.L.S., Andrade, A.G.P., Lage, G.M., & Apolinário-Souza, T. (2020). TDCS of the primary motor cortex: Learning the absolute dimension of a complex motor task. Journal of Motor Behavior, 53(4), 431444. https://doi.org/10.1080/00222895.2020.1792823

    • Search Google Scholar
    • Export Citation
  • Profeta, V.L.S., & Turvey, M.T. (2018). Bernstein’s levels of movement construction: A contemporary perspective. Human Movement Science, 57, 111133. https://doi.org/10.1016/j.humov.2017.11.013

    • Search Google Scholar
    • Export Citation
  • Singer, J.D., & Willett, J.B. (2009). Applied longitudinal data analysis: Modeling change and event occurrence. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195152968.001.0001

    • Search Google Scholar
    • Export Citation
  • Singmann, H., Bolker, B., Westfall, J., Fox, J., & Lawrence, M.A. (2017). R package ‘afex’: Analysis of factorial experiments (pp. 150). https://cran.r-project.org/web/packages/afex/index.html

    • Search Google Scholar
    • Export Citation
  • Sternad, D. (2018). It’s not (only) the mean that matters: Variability, noise and exploration in skill learning. Current Opinion in Behavioral Sciences, 20, 183195. https://doi.org/10.1016/j.cobeha.2018.01.004

    • Search Google Scholar
    • Export Citation
  • Sternad, D., Abe, M.O., Hu, X., & Müller, H. (2011). Neuromotor noise, error tolerance and velocity-dependent costs in skilled performance. PLoS Computational Biology, 7(9), Article e1002159. https://doi.org/10.1371/journal.pcbi.1002159

    • Search Google Scholar
    • Export Citation
  • Sternad, D., Park, S.W., Müller, H., & Hogan, N. (2010). Coordinate dependence of variability analysis. PLoS Computational Biology, 6(4), Article e1000751. https://doi.org/10.1371/journal.pcbi.1000751

    • Search Google Scholar
    • Export Citation
  • Tresilian, J.R., & Lonergan, A. (2002). Intercepting a moving target: Effects of temporal precision constraints and movement amplitude. Experimental Brain Research, 142(2), 193207. https://doi.org/10.1007/s00221-001-0920-9

    • Search Google Scholar
    • Export Citation
  • Tresilian, J.R., & Plooy, A. (2006). Systematic changes in the duration and precision of interception in response to variation of amplitude and effector size. Experimental Brain Research, 171(4), 421435. https://doi.org/10.1007/s00221-005-0286-5

    • Search Google Scholar
    • Export Citation
  • Tuch, D.S., Salat, D.H., Wisco, J.J., Zaleta, A.K., Hevelone, N.D., & Rosas, H.D. (2005). Choice reaction time performance correlates with diffusion anisotropy in white matter pathways supporting visuospatial attention. Proceedings of the National Academy of Sciences, 102(34), 1221212217. https://doi.org/10.1073/pnas.0407259102

    • Search Google Scholar
    • Export Citation
  • Wilson, A.D., Weightman, A., Bingham, G.P., Zhu, Q., & Author, C. (2016). Using task dynamics to quantify the affordances of throwing for long distance and accuracy. Journal of Experimental Psychology. Human Perception and Performance, 42(7), 965981.

    • Search Google Scholar
    • Export Citation
  • Zhang, Z., Guo, D., Huber, M.E., Park, S.W., & Sternad, D. (2018). Exploiting the geometry of the solution space to reduce sensitivity to neuromotor noise. PLoS Computational Biology, 14(2), Article e1006013. https://doi.org/10.1371/journal.pcbi.1006013

    • Search Google Scholar
    • Export Citation
  • Zhang, Z., & Sternad, D. (2021). Back to reality: Differences in learning strategy in a simplified virtual and a real throwing task. Journal of Neurophysiology, 125(1), 4362. https://doi.org/10.1152/JN.00197.2020

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 575 575 36
Full Text Views 93 93 5
PDF Downloads 40 40 3