Closed-loop attention restoration theory for virtual reality-based attentional engagement enhancement

Li, G. , Zhou, S., Kong, Z. and Guo, M. (2020) Closed-loop attention restoration theory for virtual reality-based attentional engagement enhancement. Sensors, 20(8), 2208. (doi: 10.3390/s20082208) (PMID:32295136) (PMCID:PMC7218885)

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Today, as media and technology multitasking becomes pervasive, the majority of young people face a challenge regarding their attentional engagement (that is, how well their attention can be maintained). While various approaches to improve attentional engagement exist, it is difficult to produce an effect in younger people, due to the inadequate attraction of these approaches themselves. Here, we show that a single 30-min engagement with an attention restoration theory (ART)-inspired closed-loop software program (Virtual ART) delivered on a consumer-friendly virtual reality head-mounted display (VR-HMD) could lead to improvements in both general attention level and the depth of engagement in young university students. These improvements were associated with positive changes in both behavioral (response time and response time variability) and key electroencephalography (EEG)-based neural metrics (frontal midline theta inter-trial coherence and parietal event-related potential P3b). All the results were based on the comparison of the standard Virtual ART tasks (control group, n = 15) and closed-loop Virtual ART tasks (treatment group, n = 15). This study provides the first case of EEG evidence of a VR-HMD-based closed-loop ART intervention generating enhanced attentional engagement.

Item Type:Articles
Additional Information:Funding: This research was sponsored by the Shanghai Sailing Program under Grant 17YF1426900.
Glasgow Author(s) Enlighten ID:Li, Dr Gang
Creator Roles:
Li, G.Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Writing – original draft, Writing – review and editing, Supervision, Project administration, Funding acquisition
Authors: Li, G., Zhou, S., Kong, Z., and Guo, M.
College/School:College of Science and Engineering > School of Psychology
Journal Name:Sensors
ISSN (Online):1424-8220
Copyright Holders:Copyright © 2020 by the authors
First Published:First published in Sensors 20(8):2208
Publisher Policy:Reproduced under a Creative Commons license

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