Literature DB >> 36163607

Exercising choice over feedback schedules during practice is not advantageous for motor learning.

Laura St Germain1, Brad McKay2, Andrew Poskus2, Allison Williams2, Olena Leshchyshen2, Sherry Feldman2, Joshua G A Cashaback3,4,5,6, Michael J Carter7.   

Abstract

The idea that there is a self-controlled learning advantage, where individuals demonstrate improved motor learning after exercising choice over an aspect of practice compared to no-choice groups, has different causal explanations according to the OPTIMAL theory or an information-processing perspective. Within OPTIMAL theory, giving learners choice is considered an autonomy-supportive manipulation that enhances expectations for success and intrinsic motivation. In the information-processing view, choice allows learners to engage in performance-dependent strategies that reduce uncertainty about task outcomes. To disentangle these potential explanations, we provided participants in choice and yoked groups with error or graded feedback (Experiment 1) and binary feedback (Experiment 2) while learning a novel motor task with spatial and timing goals. Across both experiments (N = 228 participants), we did not find any evidence to support a self-controlled learning advantage. Exercising choice during practice did not increase perceptions of autonomy, competence, or intrinsic motivation, nor did it lead to more accurate error estimation skills. Both error and graded feedback facilitated skill acquisition and learning, whereas no improvements from pre-test performance were found with binary feedback. Finally, the impact of graded and binary feedback on perceived competence highlights a potential dissociation of motivational and informational roles of feedback. Although our results regarding self-controlled practice conditions are difficult to reconcile with either the OPTIMAL theory or the information-processing perspective, they are consistent with a growing body of evidence that strongly suggests self-controlled conditions are not an effective approach to enhance motor performance and learning.
© 2022. The Psychonomic Society, Inc.

Entities:  

Keywords:  Knowledge of results; OPTIMAL theory; Retention; Self-controlled

Year:  2022        PMID: 36163607     DOI: 10.3758/s13423-022-02170-5

Source DB:  PubMed          Journal:  Psychon Bull Rev        ISSN: 1069-9384


  37 in total

1.  Self-controlled feedback is effective if it is based on the learner's performance.

Authors:  Suzete Chiviacowsky; Gabriele Wulf
Journal:  Res Q Exerc Sport       Date:  2005-03       Impact factor: 2.500

2.  Recommended effect size statistics for repeated measures designs.

Authors:  Roger Bakeman
Journal:  Behav Res Methods       Date:  2005-08

3.  Examining the impact of error estimation on the effects of self-controlled feedback.

Authors:  Joao A C Barros; Zachary D Yantha; Michael J Carter; Julia Hussien; Diane M Ste-Marie
Journal:  Hum Mov Sci       Date:  2018-12-20       Impact factor: 2.161

4.  Self-controlled knowledge of results: age-related differences in motor learning, strategies, and error detection.

Authors:  Michael J Carter; Jae T Patterson
Journal:  Hum Mov Sci       Date:  2012-11-17       Impact factor: 2.161

Review 5.  Power failure: why small sample size undermines the reliability of neuroscience.

Authors:  Katherine S Button; John P A Ioannidis; Claire Mokrysz; Brian A Nosek; Jonathan Flint; Emma S J Robinson; Marcus R Munafò
Journal:  Nat Rev Neurosci       Date:  2013-04-10       Impact factor: 34.870

6.  An interpolated activity during the knowledge-of-results delay interval eliminates the learning advantages of self-controlled feedback schedules.

Authors:  Michael J Carter; Diane M Ste-Marie
Journal:  Psychol Res       Date:  2016-02-18

7.  Effects of traditional and reversed bandwidth knowledge of results on motor learning.

Authors:  J H Cauraugh; D Chen; S J Radlo
Journal:  Res Q Exerc Sport       Date:  1993-12       Impact factor: 2.500

8.  Self-controlled feedback: does it enhance learning because performers get feedback when they need it?

Authors:  Suzete Chiviacowsky; Gabriele Wulf
Journal:  Res Q Exerc Sport       Date:  2002-12       Impact factor: 2.500

9.  Dissociating error-based and reinforcement-based loss functions during sensorimotor learning.

Authors:  Joshua G A Cashaback; Heather R McGregor; Ayman Mohatarem; Paul L Gribble
Journal:  PLoS Comput Biol       Date:  2017-07-28       Impact factor: 4.475

10.  The gradient of the reinforcement landscape influences sensorimotor learning.

Authors:  Joshua G A Cashaback; Christopher K Lao; Dimitrios J Palidis; Susan K Coltman; Heather R McGregor; Paul L Gribble
Journal:  PLoS Comput Biol       Date:  2019-03-04       Impact factor: 4.475

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