Autonomy-supportive instructional language does not enhance skill acquisition compared to controlling instructional language
DOI:
https://doi.org/10.51224/SRXIV.298Keywords:
Motor learning, Retention, OPTIMAL theory, PreregisteredAbstract
Instructional language is one of three techniques in OPTIMAL theory that can be manipulated to foster an autonomy-supportive practice environment to enhance motor performance and learning. While autonomy-supportive language has been shown to be beneficial in educational psychology, coaching, and health settings, the wording of task instructions has received minimal attention in the motor learning literature to date. We investigated the influence of two instructional language styles on skill acquisition in a preregistered experiment. Participants (N = 156) learned a speed cup stacking task and received instructions throughout practice that used either autonomy-supportive or controlling language. Although the autonomy-supportive instructions resulted in higher perceptions of autonomy, there were no group differences for motor performance in acquisition or retention. Perceptions of competence and intrinsic motivation did not differ between groups at any time point. These data are difficult to reconcile with key predictions in OPTIMAL theory regarding a direct and causal influence of motivational factors on performance and learning. However, our equivalence test suggests these effects on skill acquisition may be smaller than what we were powered to detect. These findings are consistent with a growing body of evidence highlighting the need for much larger N experiments in motor learning research.
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References
Anscombe, F. J. (1973). Graphs in statistical analysis. The American Statistician, 27 (1), 17–21. https://doi.org/10.1080/00031305.1973.10478966
Aphalo, P. J. (2022). Ggpmisc: Miscellaneous extensions to ’ggplot2’. https://CRAN.R-project.org/package=ggpmisc
Aust, F., & Barth, M. (2022). papaja: Prepare reproducible APA journal articles with R Markdown. https://github.com/crsh/papaja
Bacelar, M. F. B., Parma, J. O., Cabral, D., Daou, M., Lohse, K. R., & Miller, M. W. (2022). Dissociating the contributions of motivational and information processing factors to the self-controlled feedback learning benefit. Psychology of Sport and Exercise, 59, 102119. https://doi.org/10.1016/j.psychsport.2021.102119
Bartholomew, K. J., Ntoumanis, N., & Thoslashgersen-Ntoumani, C. (2009). A review of controlling motivational strategies from a self-determination theory perspective: Implications for sports coaches. International Review of Sport and Exercise Psychology, 2 (2), 215–233. https://doi.org/10.1080/17509840903235330
Bartoš, F., Maier, M., Wagenmakers, E.-J., Doucouliagos, H., & Stanley, T. D. (2023). Robust Bayesian meta-analysis: Model-averaging across complementary publication bias adjustment methods. Research Synthesis Methods, 14 (1), 99–116. https://doi.org/10.1002/jrsm.1594
Brysbaert, M. (2019). How many participants do we have to include in properly powered experiments? A tutorial of power analysis with reference tables. Journal of Cognition, 2 (1), 16. https://doi.org/10.5334/joc.72
Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., & Munafò, M. R. (2013). Power failure: Why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience, 14, 365–376.
Carroll, M., & Allen, J. (2021). “Zooming in” on the antecedents of youth sport coaches’ autonomy-supportive and controlling interpersonal behaviours: A multimethod study. International Journal of Sports Science & Coaching, 16 (2), 236–248. https://doi.org/10.1177/1747954120958621
Carter, M. J., Carlsen, A. N., & Ste-Marie, D. M. (2014). Self-controlled feedback is effective if it is based on the learner’s performance: A replication and extension of Chiviacowsky and Wulf (2005). Frontiers in Psychology, 5, 1–10. https://doi.org/10.3389/fpsyg.2014.01325
Carter, M. J., & Ste-Marie, D. M. (2017). Not all choices are created equal: Task-relevant choices enhance motor learning compared to task-irrelevant choices. Psychonomic Bulletin & Review, 24 (6), 1879–1888. https://doi.org/10.3758/s13423-017-1250-7
Champely, S. (2020). Pwr: Basic functions for power analysis. https://CRAN.R-project.org/package=pwr
Chiviacowsky, S., & Wulf, G. (2002). Self-controlled feedback: Does it enhance learning because performers get feedback when they need it? Research Quarterly for Exercise and Sport, 73 (4), 408–415. https://doi.org/10.1080/02701367.2002.10609040
Chiviacowsky, S., & Wulf, G. (2005). Self-controlled feedback is effective if it is based on the learner’s performance. Research Quarterly for Exercise and Sport, 76 (1), 42–48. https://doi.org/10.1080/02701367.2005.10599260
Deci, E. L., & Ryan, R. M. (2012). Self-determination theory. In Handbook of theories of social psychology (pp. 416–436). Sage Publications Ltd.
Efron, B., & Tibshirani, R. J. (1994). An introduction to the bootstrap. CRC press.
Fairclough, S., Ewing, K., & Roberts, J. (2009). Measuring task engagement as an input to physiological computing. 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, 1–9. https://doi.org/10.1109/ACII.2009.5349483
Harrell, F. E., & Davis, C. E. (1982). A new distribution-free quantile estimator. Biometrika, 69 (3), 635–640. https://doi.org/doi.org/10.2307/2335999
Harrell Jr, F. E. (2023). Hmisc: Harrell miscellaneous. https://CRAN.R-project.org/package=Hmisc
Hochberg, Y. (1988). A sharper Bonferroni procedure for multiple tests of significance. Biometrika, 75 (4), 800–802.
Hooyman, A., Wulf, G., & Lewthwaite, R. (2014). Impacts of autonomy-supportive versus controlling instructional language on motor learning. Human Movement Science, 36, 190–198. https://doi.org/10.1016/j.humov.2014.04.005
Kassambara, A. (2023). Rstatix: Pipe-friendly framework for basic statistical tests. https://CRAN.R-project.org/package=rstatix
Lakens, D. (2022). Sample size justification. Collabra: Psychology, 8 (1), 33267. https://doi.org/10.1525/collabra.33267
Leiker, A. M., Bruzi, A. T., Miller, M. W., Nelson, M., Wegman, R., & Lohse, K. R. (2016). The effects of autonomous difficulty selection on engagement, motivation, and learning in a motion-controlled video game task. Human Movement Science, 49, 326–335. https://doi.org/10.1016/j.humov.2016.08.005
Leiker, A. M., Pathania, A., Miller, M. W., & Lohse, K. R. (2019). Exploring the neurophysiological effects of self-controlled practice in motor skill learning. Journal of Motor Learning and Development, 7 (1), 13–34. https://doi.org/10.1123/jmld.2017-0051
Lenth, R. V. (2001). Some practical guidelines for effective sample size determination. The American Statistician, 55 (3), 187–193. https://doi.org/10.1198/000313001317098149
Lenth, R. V. (2023). Emmeans: Estimated marginal means, aka least-squares means. https://CRAN.R-project.org/package=emmeans
Lewthwaite, R., Chiviacowsky, S., Drews, R., & Wulf, G. (2015). Choose to move: The motivational impact of autonomy support on motor learning. Psychonomic Bulletin & Review, 22 (5), 1383–1388. https://doi.org/10.3758/s13423-015-0814-7
Liao, C.-M., & Masters, R. S. W. (2001). Analogy learning: A means to implicit motor learning. Journal of Sports Sciences, 19 (5), 307–319. https://doi.org/10.1080/02640410152006081
Lohse, K., Buchanan, T., & Miller, M. (2016). Underpowered and overworked: Problems with data analysis in motor learning studies. Journal of Motor Learning and Development, 4 (1), 37–58. https://doi.org/10.1123/jmld.2015-0010
Maier, M. (2023). Robust Bayesian meta-analysis: Addressing publication bias with model-averaging. Psychological Methods, 28 (1), 107–122.
Masters, R. S. W., van Duijn, T., & Uiga, L. (2020). Advances in implicit motor learning. In N. J. Hodges & A. M. Williams (Eds.), Skill acquisition in sport: Research, theory, and practice (3rd ed.). Routledge. https://doi.org/10.4324/9781351189750-5
McAuley, E., Duncan, T., & Tammen, V. V. (1989). Psychometric properties of the Intrinsic Motivation Inventory in a competitive sport setting: A confirmatory factor analysis. Research Quarterly for Exercise and Sport, 60 (1), 48–58. https://doi.org/10.1080/02701367.1989.10607413
McDonough, M. H., & Crocker, P. R. E. (2007). Testing self-determined motivation as a mediator of the relationship between psychological needs and affective and behavioral outcomes. Journal of Sport and Exercise Psychology, 29 (5), 645–663. https://doi.org/10.1123/jsep.29.5.645
McKay, B., Bacelar, M. F. B., Parma, J. O., Miller, M. W., & Carter, M. J. (2023). The combination of reporting bias and underpowered study designs has substantially exaggerated the motor learning benefits of self-controlled practice and enhanced expectancies: A meta-analysis. International Review of Sport and Exercise Psychology, 1–21. https://doi.org/10.1080/1750984X.2023.2207255
McKay, B., Bacelar, M. F., & Carter, M. J. (2023). On the reproducibility of power analyses in motor behavior research. Journal of Motor Learning and Development, 11 (1), 29–44.
McKay, B., Corson, A., Vinh, M.-A., Jeyarajan, G., Tandon, C., Brooks, H., Hubley, J., & Carter, M. J. (2023). Low prevalence of a priori power analyses in motor behavior research. Journal of Motor Learning and Development, 11 (1), 15–28.
McKay, B., & Ste-Marie, D. M. (2020). Autonomy support and reduced feedback frequency have trivial effects on learning and performance of a golf putting task. Human Movement Science, 71, 102612. https://doi.org/10.1016/j.humov.2020.102612
McKay, B., & Ste-Marie, D. M. (2022). Autonomy support via instructionally irrelevant choice not beneficial for motor performance or learning. Research Quarterly for Exercise and Sport, 93 (1), 64–76. https://doi.org/10.1080/02701367.2020.1795056
McKay, B., Yantha, Z. D., Hussien, J., Carter, M. J., & Ste-Marie, D. M. (2022). Meta-analytic findings in the self-controlled motor learning literature: Underpowered, biased, and lacking evidential value. Meta-Psychology, 6. https://doi.org/10.15626/MP.2021.2803
Mossman, L. H., Slemp, G. R., Lewis, K. J., Colla, R. H., & O’Halloran, P. (2022). Autonomy support in sport and exercise settings: A systematic review and meta-analysis. International Review of Sport and Exercise Psychology, 1–24. https://doi.org/10.1080/1750984X.2022.2031252
Murray, A., Hall, A. M., Williams, G. C., McDonough, S. M., Ntoumanis, N., Taylor, I. M., Jackson, B., Matthews, J., Hurley, D. A., & Lonsdale, C. (2015). Effect of a self determination theory–based communication skills training program on physiotherapists’ psychological support for their patients with chronic low back pain: A randomized controlled trial. Archives of Physical Medicine and Rehabilitation, 96 (5), 809–816. https://doi.org/10.1016/j.apmr.2014.11.007
Ng, J. Y. Y., Ntoumanis, N., Thøgersen-Ntoumani, C., Deci, E. L., Ryan, R. M., Duda, J. L., & Williams, G. C. (2012). Self-determination theory applied to health contexts: A meta-analysis. Perspectives on Psychological Science, 7 (4), 325–340. https://doi.org/10.1177/1745691612447309
Ng, J. Y. Y., Lonsdale, C., & Hodge, K. (2011). The Basic Needs Satisfaction in Sport Scale (BNSSS): Instrument development and initial validity evidence. Psychology of Sport and Exercise, 12 (3), 257–264. https://doi.org/10.1016/j.psychsport.2010.10.006
O’Brien, H. L., & Toms, E. G. (2009). The development and evaluation of a survey to measure user engagement. Journal of the American Society for Information Science and Technology, 61 (1), 50–69. https://doi.org/10.1002/asi.21229
Okada, R. (2021). Effects of perceived autonomy support on academic achievement and motivation among higher education students: A Meta-analysis. Japanese Psychological Research, jpr.12380. https://doi.org/10.1111/jpr.12380
Pedersen, T. L. (2022). Patchwork: The composer of plots. https://CRAN.R-project.org/package=patchwork
R Core Team. (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/
Re, A. C. D. (2013). Compute.es: Compute effect sizes. In R Package. https://cran.r-project.org/package=compute.es
Reeve, J. (2009). Why teachers adopt a controlling motivating style toward students and how they can become more autonomy supportive. Educational Psychologist, 44 (3), 159–175. https://doi.org/10.1080/00461520903028990
Reeve, J., Nix, G., & Hamm, D. (2003). Testing models of the experience of self-determination in intrinsic motivation and the conundrum of choice. Journal of Educational Psychology, 95 (2), 375–392. https://doi.org/10.1037/0022-0663.95.2.375
Reeve, J., & Tseng, C.-M. (2011). Cortisol reactivity to a teacher’s motivating style: The biology of being controlled versus supporting autonomy. Motivation and Emotion, 35 (1), 63–74. https://doi.org/10.1007/s11031-011-9204-2
Rousselet, G. A., Pernet, C. R., & Wilcox, R. R. (2017). Beyond differences in means: Robust graphical methods to compare two groups in neuroscience. European Journal of Neuroscience, 46 (2), 1738–1748. https://doi.org/10.1111/ejn.13610
Rousselet, G. A., Pernet, C. R., & Wilcox, R. R. (2021). The percentile bootstrap: A primer with step-by-step instructions in R. Advances in Methods and Practices in Psychological Science, 4 (1), 2515245920911881. https://doi.org/10.1177/2515245920911881
Rousselet, G. A., Pernet, C. R., & Wilcox, R. R. (2023). An introduction to the bootstrap: A versatile method to make inferences by using data-driven simulations. Meta-Psychology, 7. https://doi.org/10.15626/MP.2019.205
Rousselet, G. A., & Wilcox, R. R. (2020). Reaction times and other skewed distributions: Problems with the mean and the median. Meta-Psychology, 4, 1–39.
Ryan, R. M., & Deci, E. L. (2000). Self-Determination Theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55 (1), 68–78. https://doi.org/10.1037110003-066X.55.1.68
Ryan, R. M., & Deci, E. L. (2017). Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford Press.
Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary Educational Psychology, 61, 101860. https://doi.org/10.1016/j.cedpsych.2020.101860
Salmoni, A. W., Schmidt, R. A., & Walter, C. B. (1984). Knowledge of results and motor learning: A review and critical reappraisal. Psychological Bulletin, 95 (3), 355–386. https://doi.org/10.1037/0033-2909.95.3.355
Sanli, E. A., Patterson, J. T., Bray, S. R., & Lee, T. D. (2013). Understanding self-controlled motor learning protocols through the self-determination theory. Frontiers in Psychology, 3, 1–17. https://doi.org/10.3389/fpsyg.2012.00611
Schuirmann, D. J. (1987). A comparison of the Two One-Sided Tests Procedure and the Power Approach for assessing the equivalence of average bioavailability. Journal of Pharmacokinetics and Biopharmaceutics, 15 (6), 657–680. https://doi.org/10.1007/BF01068419
Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science, 22 (11), 1359–1366. https://doi.org/10.1177/0956797611417632
Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2012). A 21 Word Solution. https://doi.org/10.2139/ssrn.2160588
Singmann, H., Bolker, B., Westfall, J., Aust, F., & Ben-Shachar, M. S. (2023). Afex: Analysis of factorial experiments. https://CRAN.R-project.org/package=afex
St. Germain, L., McKay, B., Poskus, A., Williams, A., Leshchyshen, O., Feldman, S., Cashaback, J. G. A., & Carter, M. J. (2023). Exercising choice over feedback schedules during practice is not advantageous for motor learning. Psychonomic Bulletin & Review, 30, 621–633. https://doi.org/10.3758/s13423-022-02170-5
St. Germain, L., Williams, A., Balbaa, N., Poskus, A., Leshchyshen, O., Lohse, K. R., & Carter, M. J. (2022). Increased perceptions of autonomy through choice fail to enhance motor skill retention. Journal of Experimental Psychology: Human Perception and Performance, 48 (4), 370–379. https://doi.org/10.1037/xhp0000992
Ste-Marie, D. M., Carter, M. J., & Yantha, Z. D. (2020). Self-controlled learning: Current findings, theoretical perspectives, and future directions. In N. J. Hodges & A. M. Williams (Eds.), Skill acquisition in sport: Research, theory, and practice (3rd ed.). Routledge. https://doi.org/10.4324/9781351189750-7
Ste-Marie, D. M., Lelievre, N., & St. Germain, L. (2020). Revisiting the applied model for the use of observation: A review of articles spanning 2011–2018. Research Quarterly for Exercise and Sport, 91 (4), 594–617. https://doi.org/10.1080/02701367.2019.1693489
Su, Y.-L., & Reeve, J. (2011). A meta-analysis of the effectiveness of intervention programs designed to support autonomy. Educational Psychology Review, 23 (1), 159–188. https://doi.org/10.1007/s10648-010-9142-7
Torchiano, M. (2020). Effsize: Efficient effect size computation. https://doi.org/10.5281/zenodo.1480624
Tsagris, M., & Frangos, C. (2020). Cronbach: Cronbach’s alpha. https://CRAN.R-project.org/package=Cronbach
Ushey, K. (2023). Renv: Project environments. https://CRAN.R-project.org/package=renv
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T. L., Miller, E., Bache, S. M., Müller, K., Ooms, J., Robinson, D., Seidel, D. P., Spinu, V., . . . Yutani, H. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4 (43), 1686. https://doi.org/10.21105/joss.01686
Wilcox, R. R. (2021). Introduction to robust estimation and hypothesis testing (Fifth). Academic Press.
Wilcox, R. R., & Rousselet, G. A. (2023). An updated guide to robust statistical methods in neuroscience. Current Protocols, 3 (3), e719. https://doi.org/10.1002/cpz1.719
Wulf, G., Freitas, H. E., & Tandy, R. D. (2014). Choosing to exercise more: Small choices increase exercise engagement. Psychology of Sport and Exercise, 15 (3), 268–271. https://doi.org/10.1016/j.psychsport.2014.01.007
Wulf, G., Iwatsuki, T., Machin, B., Kellogg, J., Copeland, C., & Lewthwaite, R. (2018). Lassoing skill through learner choice. Journal of Motor Behavior, 50 (3), 285–292. https://doi.org/10.1080/00222895.2017.1341378
Wulf, G., & Lewthwaite, R. (2016). Optimizing performance through intrinsic motivation and attention for learning: The OPTIMAL theory of motor learning. Psychonomic Bulletin & Review, 23 (5), 1382–1414. https://doi.org/10.3758/s13423-015-0999-9
Yantha, Z. D., McKay, B., & Ste-Marie, D. M. (2022). The recommendation for learners to be provided with control over their feedback schedule is questioned in a self-controlled learning paradigm. Journal of Sports Sciences, 40 (7), 769–782. https://doi.org/10.1080/02640414.2021.2015945
Zhu, H. (2021). kableExtra: Construct complex table with ’kable’ and pipe syntax. https://CRAN.R-project.org/package=kableExtra
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Copyright (c) 2023 Laura St. Germain, Brad McKay, Chitrini Tandon, Jeswende Seedu, Lidia Barbera, Chantal Carrillo, Denver M.Y. Brown, Michael J. Carter (Author)
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