Beyond Curve Fitting to Inferences about Learning

In this commentary, the authors elaborate on the issue of going beyond curve fitting to the drawing of inferences about motor learning. They argue that the agenda of A. Heathcote and S. Brown (2004) is largely a theory-free, curve-fitting enterprise that can be useful for certain aspects of describing behavior change, but that its gold standard of percentage of variance accounted for can also be misleading in its relevance to the theory of learning. Clearly, analysis methods are necessary and some are better than others, but the researcher can more fully exploit the relevance of methods to the construct with a priori theorizing than with a data-driven strategy of maximizing percentage of variance accounted for in curve fitting.