Predicting individual differences in motor learning: A critical review. (October 2022)

• Record Type: Journal Article
• Authors: Ranganathan, Rajiv; Cone, Simon; Fox, Brian
• Is Part Of: Neuroscience and biobehavioral reviews. Volume 141(2022)
• Publication Date: 2022-10
• Subjects: Correlation -- Skill -- Power -- Sample size -- Pre-registration; Psychophysiology -- Periodicals; Human behavior -- Periodicals; Animal behavior -- Periodicals; Neurology -- Periodicals; Behavior -- Periodicals; Ethology -- Periodicals; Neurology -- Periodicals; Psychophysiologie -- Périodiques; Comportement humain -- Périodiques; Animaux -- Mœurs et comportement -- Périodiques; Neurologie -- Périodiques; Animal behavior; Human behavior; Neurology; Psychophysiology; Periodicals; Electronic journals; 573.8
• DOI: 10.1016/j.neubiorev.2022.104852
• Languages: English
• ISSNs: 0149-7634

Abstract

Abstract: The ability to predict individual differences in motor learning has significant implications from both theoretical and applied perspectives. However, there is high variability in the methodological and analytical strategies employed as evidence for such predictions. Here, we critically examine the evidence for predictions of individual differences in motor learning by reviewing the literature from a 20-year period (2000–2020). Specifically, we examined four factors: (i) the predictor and predicted variables used, (ii) the strength of the prediction and associated sample size, (iii) the timescale over which the prediction was made, and (iv) the type of motor task used. Overall, the results highlight several issues that raise concerns about the quality of the evidence for such predictions. First, there was a large variation in both predictor and predicted variables, suggesting the presence of a large number of researcher degrees of freedom. Second, sample sizes tended to be small, and the strength of the correlation showed an inverse relation with sample size. Third, the timescale of most predictions was very short, mostly constrained to a single day. Last, most studies were largely restricted to two experimental paradigms – adaptation and sequence learning. Based on these issues, we highlight recommendations for future studies to improve the quality of evidence for predicting individual differences in motor learning. Highlights: We reviewed the evidence for predicting individual differences in motor learning. There was a large variation in predicted and predictor variables. The size of correlations was inversely related to the sample size. Prediction time spans were short and restricted to two main task types. We suggest recommendations for future studies in light of these issues.