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

OPTIMAL theory's claims about motivation lack evidence in the motor learning literature

Motivation is commonly recognized by researchers and practitioners as a key factor for motor learning. The OPTIMAL theory of motor learning (Wulf & Lewthwaite, 2016) claims that practice conditions that enhance learners' expectancies for future successful outcomes or that are autonomy supportive are motivating, thus leading to better learning. To examine the current evidence of the association between motivation and motor learning, we searched the literature for studies that manipulated expectancies and/or autonomy support. Specifically, our goals were to assess whether these manipulations resulted in group differences in motivation and, if so, whether increased motivation was associated with learning advantages. Results showed that out of 166 experiments, only 21% (n = 35) included at least one measure of motivation, even though this is the main factor proposed by OPTIMAL theory to explain the learning benefits of these manipulations. Among those, only 23% (n = 8) found group-level effects on motivation, suggesting that these manipulations might not be as motivating as expected. Of the eight experiments that found a group-level effect on motivation, five also observed learning benefits, offering limited evidence that when practice conditions increase motivation, learning is more likely to occur. Overall, the small number of studies assessing motivation precludes any reliable conclusions on the association between motivation and motor learning from being drawn. Together, our results question whether manipulations implemented in the research lines supporting OPTIMAL theory are indeed motivating and highlight the lack of sufficient evidence in these literatures to support that increased motivation benefits motor learning.

Self-Controlled Feedback in Motor Learning: The Effects Depend on the Frequency of Request

The benefits of allowing learners to control when to receive knowledge of results (KR) compared to a yoked group has been recently challenged and postulated to be mild at best. A potential explanation for such dissident findings is that individuals differentially utilize the autonomy provided by the self-controlled condition, which, in its turn, affects the outcomes. Therefore, the present study investigated the effects of self-controlled KR on motor learning focusing on the frequency of KR requests when performing an anticipatory timing task. Self-controlled groups were created based on participants' KR frequency of request (High, Medium, and Low referring to fifth, third, and first quintile) and, then, Yoked groups were created self-control condition pairing the KR request of the Self-controlled groups. We also measured self-efficacy and processing time as means to verify potential correlates. The results supported the expected interaction. While no difference between self-controlled and yoked groups were found for low frequencies of KR, a moderate amount of KR request was related to better results for the self-controlled group. Nonetheless, the opposite trend was observed for high frequencies of KR; the yoked group was superior to the self-controlled group. The results of this study allow us to conclude that the choices made, and not just the possibility of choosing, seem to define the benefits of KR self-control in motor learning.

Unpacking the black box of voice therapy: A clinical application and revision of the Motor Learning Classification Framework (MLCF)

PURPOSE

Voice therapy is a complex behavioural intervention. Understanding its components is integral for continued advancement of voice therapy research, translation of evidence into the clinical setting and improved client care. The Motor Learning Classification Framework (MLCF) offers an excellent opportunity for increasing such knowledge, specifically in relation to identifying variables that affect motor learning (ML), an important mechanism hypothesised to bring about voice change during voice therapy. The MLCF has shown promising results in identifying speech-language pathologists' (SLPs) use of ML variables during experimentally controlled voice therapy contexts. The purpose of this study was to test the feasibility of applying the framework in the clinical context of everyday voice therapy practice.

METHOD

Data consisted of two video-recorded voice therapy sessions representing usual voice therapy care. Classification of ML variables used by SLPs during the recorded sessions was attempted based on the MLCF.

RESULT

Several problematic features of the framework were identified. Based on deliberations between the authors of the current paper, the MLCF was revised using an iterative process. This resulted in the construction of an updated version of the framework (MLCF-V2). The MLCF-V2 organises ML strategies into two broad categories: directly observable behaviours and learning processes. The framework incorporates greater consideration of theory and empirical evidence supporting motivational, attentional focus and subjective error estimation influences on ML. Several examples of each ML variable are included as well as an attempt to provide clearer classification instruction.

CONCLUSION

It is anticipated that the MLCF-V2 will provide a more useful and reliable classification for use in future investigations of SLPs' use of ML variables during usual voice therapy practice.

Expectation of Teaching and Self-Controlled KR in Motor Skills Acquisition: Are There Additive Effects?

Purpose: Recent evidence suggests learning a motor skill with the expectation of teaching it enhances motor learning. The mechanisms underlying this effect seem to be similar to those of another motor learning condition, the self-control of knowledge of results (KR). Considering the similarities between the mechanisms that underlie these conditions, we aimed to investigate the learning effects obtained through expected teaching and self-controlled conditions, and whether these effects would be additive. Methods: Participants practiced a dart-throwing task under one of the following conditions: a) expecting to teach the skill; b) controlling the KR request; c) combining the two previous conditions; and d) receiving KR in a yoked condition with self-controlled participants. In acquisition phase, motivational aspects, strategies for requesting KR and aspects related to the expectation of teaching were assessed according to each condition. Results: Participants with control over KR and/or with the expectation of teaching the skill showed superior learning of the task compared to the control condition. However, the combination of the experimental conditions did not result in additive learning benefits. Increased perceived competence was found in expecting to teach, self-controlled and combined conditions, compared to the yoked group. Additionally, expecting to teach also affected the way and the frequency learners requested KR. Conclusions: Our findings provide important insights toward understanding the effects of expecting to teach, in addition to demonstrating that expecting to teach affects self-controlled KR scheduling and its use during motor skill acquisition.

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

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.

Reduction of Feedback Availability Limits Self-Control Effects

A growing body of research has demonstrated that providing learners with self-control over aspects of the learning environment facilitates the learning of a motor skill. In applied group settings, however, the provision of feedback is at times constrained by factors such as instructor availability. The purpose of the present study, therefore, was to examine how learners ostensibly provided self-control over feedback responded when the actual availability of feedback was constrained by a predetermined schedule of a virtual coach's availability to provide feedback. Participants were divided into four feedback groups and completed 72 practice trials of a sequential key-pressing task, with three different goal movement times (900, 1,200, 1,500 ms). The KR100 group received knowledge of results (KR) after every practice trial. The KR50 group received KR on an evenly distributed quasi-randomly determined schedule after 50% of the trials. The SC group had the opportunity to request KR after every trial, but KR was only available for 50% of practice trials according to the same schedule used for the KR50 group. The YK (i.e., yoked) group received KR according to the schedule of KR received by counterparts in the SC group. Approximately 24 h after acquisition, each participant returned to complete retention and transfer tests. The retention test consisted of 15 no-KR trials of the acquisition tasks (five trials for each goal time−900, 1,200, 1,500 ms). The transfer test consisted of 15 no-KR trials with new time goals (1,300, 1,600, 1,900 ms). Results revealed a significantly lower absolute constant error (ACE) score for the SC group during transfer (p < 0.05), suggesting that SC effects can occur in a reduced feedback availability environment. Other measures, however, failed to show significant advantages for the SC group during any phase of the study suggesting that effects were not as robust as previous research has indicated. The results also provided some indication that perceived scarcity might have played a role in elevating the number of feedback requests in response to the reduced autonomy environment.

The recommendation for learners to be provided with control over their feedback schedule is questioned in a self-controlled learning paradigm

Evidence that self-controlled feedback schedules are more effective for motor learning than yoked or predetermined schedules has been used to forward the recommendation that practitioners should provide choice to learners over when they would like to receive feedback. This recommendation can be questioned because the typical comparison groups in such experimentation do not well represent the applied setting. Consequently, comparison groups that better map onto the applied setting are needed. To this end, three groups learned a golf putting task: (1) self-controlled, (2) traditional-yoked, and (3) a group who were led to believe their KR schedule was being controlled by a golf coach. Participants (N = 60) completed a pre-test, acquisition phase, and delayed post-tests (retention/transfer). No group differences during the post-tests for mean radial error, F(2, 54) = 2.71, p = .075, or bivariate variable error, F(2, 56) = 0.11, p = .896, were found. Thus, the typical self-controlled learning advantage was not observed. Given the failure to replicate self-controlled benefits, combined with the fact there is little research that has directly compared self-controlled feedback schedules to coach-controlled schedules, we argue more evidence is needed before advocating that learners be provided choice over their feedback schedule.

Using error-estimation to probe the psychological processes underlying contextual interference effects

Although the learning benefits of interleaved practice schedules relative to blocked schedules are well-reported, the mechanisms underlying these effects are not fully understood. Researchers have generally suggested that random schedules of practice increase task-related information processing which arises due to switching between variations of the same task (or switching between different tasks). Thus, one potentially useful way to both probe and manipulate contextual interference is to pair it with error estimation during practice. Forced error estimation increases task-related information processing and recording these estimates provides insight into learners' self-awareness of their errors. In the present study, 84 participants were randomly allocated to four groups. Participants practiced a timing task under blocked or random schedules, with and without error estimations prior to feedback. During the acquisition phase, three target times were trained (1500, 1700, 1900 ms), with feedback delivered after every trial. We used delayed post-tests (24 hrs later) to evaluate the retention of these target times and their transfer to two new target times (1600, 1800 ms). Participants who practiced with a random schedule performed worse (i.e., greater absolute error) than those with a blocked schedule during acquisition (p = .006); however, randomly scheduled participants also showed reduced error (p = .004) on the retention and transfer tests. Although prompting error estimations led to greater self-reported mental effort being invested on the task (p = .001), error estimation was not reliably associated with superior learning (p = .133). The accuracy of error estimations did not differ as a function of practice structure (p = .070), although the accuracy of error estimations improved during acquisition (p = .006). Findings highlight the robustness of the contextual interference effect, but we did not find evidence that error estimations moderated the effect on this task. It is in some ways surprising that we found an effect of contextual interference, as past-work suggests that interference effects are attenuated (or eliminated) when participants switch between different parameters of the same task. We speculate that this might be due to the difficulty of the task; even though participants switched between parametric variations of the same task, the distinction between parameters was subtle (i.e., tenths of a second).

Effects of self-controlled knowledge of performance on motor learning and self-efficacy: A kinematic study

Study aim: To evaluate the effects of providing the learners with self-controlled knowledge of performance (KP) on motor learning and self-efficacy (SE) in a dart-throwing motor task.

Material and methods: The participants were thirty female university students who were divided into two groups including self-control and yoked groups. Participants performed five blocks of five trials in the acquisition phase, and retention and transfer tests of 10 trials one day later. Intra-limb coordination patterns (movement pattern), throwing scores, and SE (both movement pattern and movement outcome) were measured as dependent variables. Independent t test and one-way ANOVA with repeated measures were used as statistical tests.

Results: The self-controlled group performed significantly better than yoked group on movement pattern during the acquisition phase (F1,28 = 24.239, p = 0.001) and the retention test (t28 = –3.074, p = 0.007). However, there were no significant differences between groups in terms of throwing scores and SE during the acquisition, retention, and transfer phases (all p > 0.05).

Conclusion: Providing self-controlled KP can improve learning of movement pattern in the novices but do not necessary increase movement outcome or SE.

Information Processing and Technical Knowledge Contribute to Self-Controlled Video Feedback for Children Learning the Snatch Movement in Weightlifting

Our aim in this study was to examine, via technical performance analysis and speed of execution, whether information processing and technical knowledge help explain learning benefits of self-controlled video feedback in children's weightlifting performance. We randomized 24 children (aged 10 to 12 years) into self-controlled (SC) and yoked (YK) feedback groups. Learners underwent test sessions one week before (pre-test) and one day after (post-test) six weightlifting training sessions. During each test session, we recorded kinematic parameters of snatch performance using Kinovea version 0.8.15 software. After the learning sessions, the SC group improved on most kinematic parameters (e.g., the horizontal displacement of the bar between the first and the second pulls [M_DXV = 25.42%, SD = 18.96, p = 0.003) and the maximum height reached by the bar (M_HMV = 5.51%, SD = 7.71, p < 0.05)], while the YK group improved only on the DxV (M_DXV = 19.08%, SD = 24.68, p < 0.05). In addition, the SC group showed a more advanced phase of cognitive processing compared to the YK group, and the SC group showed a superior improvement in their technical knowledge level (p < 0.001) compared to the YK group (p < 0.05). Thus, key elements to correcting motor errors in children's weightlifting through self-controlled feedback were improvements in information processing and technical knowledge.

Distance Motor Learning during the COVID-19 Induced Confinement: Video Feedback with a Pedagogical Activity Improves the Snatch Technique in Young Athletes

The purpose of the present study was to investigate which of two strategies, Video Feedback with Pedagogical Activity (VF-PA) or Video Feedback (VF), would be more beneficial for the remote error correction of the snatch weightlifting technique during the confinement period. Thirty-five school aged children with at least three months of weightlifting experience were randomized to one of three training conditions: VF-PA, VF or the Control group (CONT). Subjects underwent test sessions one week before (T0) and one day after (T1) a six-session training period and a retention test session a week later (T2). During each test session, the Kinovea version 0.8.15 software measured the kinematic parameters of the snatch performance. Following distance learning sessions (T1), the VF-PA improved various kinematic parameters (i.e., barbell horizontal displacements, maximum height, looping and symmetry) compared with T0 (p < 0.5; Cohen's d = 0.58-1.1). Most of these improvements were maintained during the retention test (T2) (p<0.01, Cohen's d = 1.2-1.3) when compared withT0. However, the VF group improved only twoparameters (i.e., barbell symmetry and horizontal displacement) at T1 (p < 0.05; Cohen's d = 0.9), which were not maintained at T2. Better horizontal displacement and looping values were registered during the retention test in the VF-PA group compared with theCONT group (p < 0.05, Cohen's d = 1.49-1.52). The present findings suggest combining video feedback with pedagogical activity during the pandemic induced online coaching or physical education to improve movement learning in school aged children.

Self-controlled practice and nudging during structural learning of a novel control interface

Self-controlled practice schedules have been shown to enhance motor learning in several contexts, but their effectiveness in structural learning tasks, where the goal is to eventually learn an underlying structure or rule, is not well known. Here we examined the use of self-controlled practice in a novel control interface requiring structural learning. In addition, we examined the effect of ‘nudging’–i.e., whether altering task difficulty could influence self-selected strategies, and hence facilitate learning. Participants wore four inertial measurement units (IMUs) on their upper body and the goal was to use motions of the upper body to move a screen cursor to different targets presented on the screen. The structure in this task that had to be learned was based on the fact that the signals from the IMUs were linearly mapped to the x- and y- position of the cursor. Participants (N = 62) were split into 3 groups (random, self-selected, nudge) based on whether they had control over the sequence in which they could practice the targets. To test whether participants learned the underlying structure, participants were tested both on the trained targets, as well as novel targets that were not practiced during training. Results showed that during training, the self-selected group showed shorter movement times relative to the random group, and both self-selected and nudge groups adopted a strategy of tending to repeat targets. However, in the test phase, we found no significant differences in task performance between groups, indicating that structural learning was not reliably affected by the type of practice. In addition, nudging participants by adjusting task difficulty did not show any significant benefits to overall learning. These results suggest that although self-controlled practice influenced practice structure and facilitated learning, it did not provide any additional benefits relative to practicing on a random schedule in this task.