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There is some evidence that people learn academic (declarative) information better when studying with the expectation of having to teach, but this has not been demonstrated for perceptual-motor skills, which also rely on declarative information but more heavily on procedural knowledge. To address this possibility, participants studied golf-putting instructions and practiced putting with the expectation of having to teach another participant how to putt or the expectation of being tested on their putting. One day later, learning was assessed by testing all participants on their golf putting. Results revealed that expecting to teach enhanced learning, even after controlling for the amount of studying and practicing. Therefore, we have presented the first findings that expecting to teach enhances motor learning. Taking these findings together with similar studies focusing on declarative information, we suggest that expecting to teach yields a general learning benefit to different types of skills.

Appropriate statistical analysis is essential for accurate and reliable research. Statistical practices have an immediate impact on the perceived results of a single study but also remote effects on the dissemination of information among scientists and the cumulative nature of research. To accurately quantify potential problems facing the field of motor learning, we systematically reviewed publications from seven journals over the past 2 years to find experiments that tested the effects of different training conditions on delayed retention and transfer tests (i.e., classic motor learning paradigms). Eighteen studies were included. These studies had small sample sizes (Mdn n/group = 11.00, interquartile range [IQR]= 9.6–15.5), multiple dependent variables (Mdn = 2, IQR = 2–4), and many statistical tests per article (Mdn = 83.5, IQR = 55.8–112.5). The observed effect sizes were large (d = 0.71, IQR = 0.49, 1.11). However, the distribution of effect sizes was biased, t(16) = 3.48, p < .01. These metadata indicate problems with the way motor learning research is conducted (or at least published). We recommend several potential solutions to address these issues: a priori power calculations, prespecified analyses, data sharing, and dissemination of null results. Furthermore, we hope these data will spark serious action from all stakeholders (researchers, editorial boards, and publishers) in the field.

We surveyed researchers in the health and exercise sciences to explore different areas and magnitudes of bias in researchers’ decision making. Participants were presented with scenarios (testing a central hypothesis with p = .06 or p = .04) in a random order and surveyed about what they would do in each scenario. Participants showed significant bias in deciding to randomize additional participants when p = .06 (24.5%) compared to when p = .04 (6.5%), χ2 = 19.86, odds ratio (OR) = 13.5. Based on this bias to randomize additional participants, we present statistical simulations showing the cumulative negative effect this bias has on the false positive rate for a field. While it is important for researchers to have flexibility in their data-processing decisions, there is a cost/benefit trade-off to experimenter degrees of freedom and it is important that these decisions be unbiased. These data highlight the importance of a priori analysis plans in research.

It was tested whether learners who choose when to receive augmented feedback while practicing a motor skill exhibit enhanced augmented feedback processing and intrinsic motivation, along with superior learning, relative to learners who do not control their feedback. Accordingly, participants were assigned to either self-control (Self) or yoked groups and asked to practice a non-dominant arm beanbag toss. Self participants received augmented feedback at their discretion, whereas Yoked participants were given feedback schedules matched to Self counterparts. Participants' visual feedback was occluded, and when they received augmented feedback, their processing of it was indexed with the electroencephalography-derived feedback-related negativity (FRN). Participants self-reported intrinsic motivation via the Intrinsic Motivation Inventory (IMI) after practice, and completed a retention and transfer test the next day to index learning. Results partially support the hypothesis. Specifically, Self participants reported higher IMI scores, exhibited larger FRNs, and demonstrated better accuracy on the transfer test, but not on the retention test, nor did they exhibit greater consistency on the retention or transfer tests. Additionally, post-hoc multiple regression analysis indicated FRN amplitude predicted transfer test accuracy (accounting for IMI score). Results suggest self-controlled feedback schedules enhance feedback processing, which enhances the transfer of a newly acquired motor skill.