observing an expert guitarist playi

observing an expert guitarist playing a scale (a familiar sequence ofactions for both novice and expert guitarists), the player may use all herfingers to achieve this goal. As such, the actions performed by an expertguitarist to play a scale might look much different to those performedby a novice guitarist to play the same scale (i.e., a novice might usefewerfingers and/or transition between notes more awkwardly), eventhough the musical outcome (playing a scale) remains the same.Therefore, the ability to predict others’actions is subject to continualevaluation, and, at times, reassessment of predictions (c.f.Shadmehrand Holcomb, 1997). A quadratic function may thus not fully capturethe dynamic nature of learning, prediction, and experience-drivenchanges in AON engagement. When considering a quadratic framingof the AON engagement and familiarity relationship, a questionremains concerning what happens to AON engagement during thetrough of the curve. One possibility is that ongoing evaluation ofpredicted and actual actions manifests as local reductions in activitywithin a testing session due to practice, in line with Neural Efficiency(NE) effects (Babiloni et al., 2010; Kelly and Garavan, 2005; Wiestlerand Diedrichsen, 2013). In keeping with this prior work on neuralefficiency, we might expect that reduced activity within a testingsession should recover during subsequent testing sessions, and thenreduce again as familiarity and experience continue to accrue. Thisconceptualisation, combining the predictive coding theoretical accountwith notions of neural efficiency, would create a cubic shaped responseof AON engagement. To our knowledge, these three framings of therelationship between familiarity and AON engagement (i.e., directmatching vs. predictive coding vs. pre