Motor Performance, Mental Workload and Self-Efficacy Dynamics during Learning of Reaching Movements throughout Multiple Practice Sessions. (15th December 2019)

• Record Type: Journal Article
• Authors: Shuggi, Isabelle M.; Oh, Hyuk; Wu, Helena; Ayoub, Maria J.; Moreno, Arianna; Shaw, Emma P.; Shewokis, Patricia A.; Gentili, Rodolphe J.
• Is Part Of: Neuroscience. Volume 423(2019)
• Page Start: 232
• Page End: 248
• Publication Date: 2019-12-15
• Subjects: visuomotor learning -- mental workload -- self-efficacy -- reaching movements -- human-robot interface -- assistive technologies; Neurochemistry -- Periodicals; Neurophysiology -- Periodicals; Neurology -- Periodicals; Neurochimie -- Périodiques; Neurophysiologie -- Périodiques; Neurochemistry; Neurophysiology; Electronic journals; Periodicals; Electronic journals; 612.8
• DOI: 10.1016/j.neuroscience.2019.07.001
• Languages: English
• ISSNs: 0306-4522

Abstract

Highlights: Throughout learning, performance improves faster than attenuation of mental workload. During learning mental workload is reduced faster than self-efficacy improvements. Skill learning results in the emergence of a cognitive-psycho-motor efficiency. Abstract: The human capability to learn new motor skills depends on the efficient engagement of cognitive-motor resources, as reflected by mental workload, and psychological mechanisms (e.g., self-efficacy). While numerous investigations have examined the relationship between motor behavior and mental workload or self-efficacy in a performance context, a fairly limited effort focused on the combined examination of these notions during learning. Thus, this study aimed to examine their concomitant dynamics during the learning of a novel reaching skill practiced throughout multiple sessions. Individuals had to learn to control a virtual robotic arm via a human-machine interface by using limited head motion throughout eight practice sessions while motor performance, mental workload, and self-efficacy were assessed. The results revealed that as individuals learned to control the robotic arm, performance improved at the fastest rate, followed by a more gradual reduction of mental workload and finally an increase in self-efficacy. These results suggest that once the performance improved, less cognitive-motor resources were recruited, leading to an attenuated mental workload. Considering that attention is a primary cognitive resource driving mental workload, it is suggested that during early learning, attentional resources are primarily allocated to address task demands and not enough are available to assess self-efficacy. However, as the performance becomes more automatic, a lower level of mental workload is attained driven by decreased recruitment of attentional resources. These available resources allow for a reliable assessment of self-efficacy resulting in a subsequent observable change. These results are also discussed in terms of the application to the training and design of assistive technologies.