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01-02-2025 | Research

Autonomy-supportive instructional language does not enhance skill acquisition compared to controlling instructional language

Auteurs: Laura St. Germain, Brad McKay, Lidia Barbera, Chitrini Tandon, Jeswende Seedu, Chantal Carrillo, Denver M. Y. Brown, Michael J. Carter

Gepubliceerd in: Psychological Research | Uitgave 1/2025

Abstract

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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We report the t-test as a secondary analysis for the interested reader.

The reviewed instructions (Dr. Johnmarshall Reeve, personal communication, April 20 2022) referred to a floor curling task rather than the cup stacking task that was ultimately used in the experiment.

Parma et al., 2024 also reported that only 21% (35 out of 166) of studies testing the motivation pillar of OPTIMAL theory actually included at least one measure of motivation.

Anscombe, F. J. (1973). Graphs in statistical analysis. The American Statistician, 27(1), 17–21. https://doi.org/10.1080/00031305.1973.10478966CrossRef

Aphalo, P. J. (2022). Ggpmisc: Miscellaneous extensions to ‘ggplot2’ [R package version 0.5.2]. https://CRAN.R-project.org/package=ggpmisc

Bacelar, M. F., 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.2021.102119CrossRef

Bartholomew, K. J., Ntoumanis, N., & Thøgersen-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.1594CrossRefPubMed

Binks, J., Wilson, C. J., Van Schaik, P., & Eaves, D. (2023). Motor learning without physical practice: The effects of combined action observation and motor imagery practice on cup-stacking speed. Psychology of Sport and Exercise, 68, 102468.CrossRefPubMed

Brillinger, M., Karlinsky, A., & Patterson, J. (2024). Examining learner-controlled role-switching in dyad practice for the learning of a speed cup-stacking task. Journal of Motor Behavior, 56(2), 211–225.CrossRefPubMed

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.72CrossRefPubMedPubMedCentral

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, 365–76.CrossRefPubMed

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/10.1177/1747954120958621CrossRef

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.2014.01325CrossRef

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/s13423-017-1250-7CrossRef

Champely, S. (2020). Pwr: Basic functions for power analysis [R package version 1.3-0]. 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/10.1080/02701367.2002.10609040CrossRefPubMed

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/10.1080/02701367.2005.10599260CrossRefPubMed

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.CrossRef

Fairclough, S. H., Ewing, K. C., & 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

Granados, C., & Wulf, G. (2007). Enhancing motor learning through dyad practice: Contributions of observation and dialogue. Research Quarterly for Exercise and Sport, 78(3), 197–203.PubMed

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 [R package version 5.1-0]. https://CRAN.R-project.org/package=Hmisc

Hebert, E. (2018). The effects of observing a learning model (or two) on motor skill acquisition. Journal of Motor Learning and Development, 6(1), 4–17.CrossRef

Hochberg, Y. (1988). A sharper Bonferroni procedure for multiple tests of significance. Biometrika, 75(4), 800–802.CrossRef

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.2014.04.005CrossRefPubMed

Kassambara, A. (2023). Rstatix: Pipe-friendly framework for basic statistical tests [R package version 0.7.2]. https://CRAN.R-project.org/package=rstatix

Lakens, D. (2022). Sample size justification. Collabra. Psychology, 8(1), 33267. https://doi.org/10.1525/collabra.33267CrossRef

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.2016.08.005CrossRefPubMed

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.2017-0051CrossRef

Lenth, R. V. (2001). Some practical guidelines for effective sample size determination. The American Statistician, 55(3), 187–193. https://doi.org/10.1198/000313001317098149CrossRef

Lenth, R. V. (2023). Emmeans: Estimated marginal means, aka least-squares means [R package version 1.8.5]. 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/s13423-015-0814-7CrossRef

Liao, C.-M., & Masters, R. S. (2001). Analogy learning: A means to implicit motor learning. Journal of Sports Sciences, 19(5), 307–319. https://doi.org/10.1080/02640410152006081CrossRefPubMed

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.2015-0010CrossRef

Maier, M. (2023). Robust Bayesian meta-analysis: Addressing publication bias with model-averaging. Psychological Methods, 28(1), 107–122.CrossRefPubMed

Masters, R. S., van Duijn, T., & Uiga, L. (2020, November). Advances in implicit motor learning. In N. Hodges & A. 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/10.1080/02701367.1989.10607413CrossRefPubMed

McDonough, M. H., & Crocker, P. R. (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.29.5.645CrossRefPubMed

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. https://doi.org/10.1123/jmld.2022-0061CrossRef

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. https://doi.org/10.1123/jmld.2022-0042CrossRef

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.2020.102612CrossRefPubMed

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/10.1080/02701367.2020.1795056CrossRefPubMed

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.2014.11.007CrossRefPubMed

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/10.1177/1745691612447309CrossRefPubMed

Ng, J. 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.2010.10.006CrossRef

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.21229CrossRef

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

Parma, J. O., Miller, M. W., & Bacelar, M. F. (2024). Optimal theory’s claims about motivation lack evidence in the motor learning literature. Psychology of Sport and Exercise, 102690.

Pedersen, T. L. (2022). Patchwork: The composer of plots [R package version 1.1.2]. https://CRAN.R-project.org/package=patchwork

R Core Team. (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. https://www.R-project.org/

Re, A. C. D. (2013). Compute.es: Compute effect sizes [R package version 0.2-5]. 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/10.1080/00461520903028990CrossRef

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/10.1037/0022-0663.95.2.375CrossRef

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/s11031-011-9204-2CrossRef

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.13610CrossRefPubMed

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/10.1177/2515245920911881CrossRef

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.CrossRef

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.CrossRef

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.2020.101860CrossRef

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/10.1037/0033-2909.95.3.355CrossRefPubMed

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(JAN), 1–17. https://doi.org/10.3389/fpsyg.2012.00611

Schmidt, R. A., & Lee, T. D. (2019). Motor learning and performance: From principles to application (6th ed.). Human Kinetics Publishers.

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/BF01068419CrossRefPubMed

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/10.1177/0956797611417632CrossRefPubMed

Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2012). A 21 Word Solution (tech. rep.). Rochester, NY. 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 [R package version 1.3-0]. 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., Carter, M., & Yantha, Z. (2020, November). Self-controlled learning: Current findings, theoretical perspectives, and future directions. In N. Hodges & A. 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/s10648-010-9142-7CrossRef

Torchiano, M. (2020). Effsize: Efficient effect size computation [R package version 0.8.1]. https://doi.org/10.5281/zenodo.1480624

Tsagris, M., & Frangos, C. (2020). Cronbach: Cronbach’s alpha [R package version 0.1]. https://CRAN.R-project.org/package=Cronbach

Ushey, K. (2023). Renv: Project environments [R package version 0.17.2]. https://CRAN.R-project.org/package=renv

Van Zant, A. B., & Berger, J. (2020). How the voice persuades. Journal of Personality and Social Psychology, 118(4), 661.CrossRefPubMed

Weinstein, N., Zougkou, K., & Paulmann, S. (2018). You ‘have’ to hear this: Using tone of voice to motivate others. Journal of Experimental Psychology: Human Perception and Performance, 44(6), 898.PubMed

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., â € ¦ 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/cpz1.719CrossRefPubMed

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.2014.01.007CrossRef

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/10.1080/00222895.2017.1341378CrossRefPubMed

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/s13423-015-0999-9CrossRef

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/10.1080/02640414.2021.2015945CrossRefPubMed

Zhu, H. (2021). Kableextra: Construct complex table with ‘kable’ and pipe syntax [R package version 1.3.4]. https://CRAN.R-project.org/package=kableExtra

Titel: Autonomy-supportive instructional language does not enhance skill acquisition compared to controlling instructional language
Auteurs: Laura St. Germain
Brad McKay
Lidia Barbera
Chitrini Tandon
Jeswende Seedu
Chantal Carrillo
Denver M. Y. Brown
Michael J. Carter
Publicatiedatum: 01-02-2025
Uitgeverij: Springer Berlin Heidelberg
Gepubliceerd in: Psychological Research / Uitgave 1/2025
Print ISSN: 0340-0727
Elektronisch ISSN: 1430-2772
DOI: https://doi.org/10.1007/s00426-024-02059-z

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