A Workload Comparison During Anatomical Training With A Physical Or Virtual Model

Keywords

Anatomical training; fNIRS; Methods and metrics for testing and evaluating augmented cognition system; Physical model; Physiological response; Virtual reality; Visualization technologies; Workload

Abstract

Recent research argues for the supplementation of traditional anatomical training with emerging three-dimensional visualization technologies (3DVTs); however, little is known regarding the effect these technologies have on learner workload. In this experiment, sixty-one participants studied gross brain anatomy using either a plastic physical model (PM; n = 29) or models presented in virtual reality (VR; n = 32). Participants were fitted with a functional near-infrared spectroscopy (fNIRS) sensor, worn on the prefrontal cortex. fNIRS measures regional saturation of oxygen (RSO2) and is indicative of workload. Participants then completed a pre-knowledge test on human brain anatomy. Participants were given 10 min to use the provided 3DVT to study 16 anatomical brain structures. Following the study period, participants completed additional surveys measuring workload, newly acquired anatomical knowledge, and cognitive resources used. Overall, anatomical knowledge increased at post-test and the change was no different between PM and VR conditions. Participants in the PM condition reported significantly higher levels of spatial workload, mental demand, and frustration. RSO2 values suggest left hemispheric increases from baseline during learning for the VR condition, but decreases for the PM condition. No other measures revealed differences between the two conditions. These results provide support for the supplementation of traditional anatomical training techniques with virtual reality technology as a way of alleviating workload. Further research is needed to explain the link between workload and performance in anatomical knowledge acquisition.

Publication Date

1-1-2018

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

10916 LNAI

Number of Pages

240-252

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/978-3-319-91467-1_20

Socpus ID

85050460899 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85050460899

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