Abstract
Subtle changes in task instructions with associated changes of one’s focus of attention (FOA) can have measurable effects on performance and learning of complex motor skills (Wulf, 2013). The beneficial effects of adopting an external focus of attention (EF) have been well-documented in physical environments, and recently have also been demonstrated in virtual reality (VR) environments (Cochran, Aiken, Rhea, & Raisbeck, 2021; Yamada, Kuznetsov, Diekfuss, & Raisbeck, 2021). However, these studies primarily focused on performance outcomes (such as error and accuracy), with relatively less emphasis on movement coordination. Given that most human movements are motor abundant (more degrees of freedom (DOF) contributing to the task than are strictly necessary to complete it) (Gefland & Latash, 1998), FOA instructions may change how motor abundance is utilized by the central nervous system (CNS). The primary purpose of this dissertation was to examine the potential effects of focus of attention (FOA) on how the CNS utilizes motor abundance in a reaching task under the uncontrolled manifold analysis (UCM). Chapter 1 provides a brief introduction on FOA and motor abundance. Chapter 2 provides an in-depth review on FOA, movement kinematics, inter-joint coordination, variability, and current approaches to study movement variability and inter-joint coordination in motor control. Chapter 3 investigates attentional focus effects on joint covariation in a reaching task. It was shown EF led to lower mean radial error in reaching to a stationary target than internal focus (IF). Consistent with this result, the UCM analysis showed that EF led to lower goal-relevant variance among the joints (VORT) compared to IF during the reach. The goal-irrelevant variance (VUCM) did not show FOA effects but was lower when reaching was performed with the dominant limb. The index of stability of joint coordination with respect to endpoint position (ΔVz) was not different between the EF and IF. Chapter 4 investigates FOA effects on joint covariation in a virtual reality reaching task. As Chapter 3’s results have shown, EF led to lower mean radial error than IF. In line with these results, similar to Chapter 3, the UCM analysis showed EF led to lower goal-relevant variance among joints (VORT) compared to IF during the reach. No difference was found between FOA conditions or between dominance groups for VUCM or index of stability (ΔV). Chapter 5 concludes that AF does influence joint coordination, specifically, IF disrupts the goal relevant join covariation without affecting goal-irrelevant coordination.
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