Dynamic Cognitive Load Assessment in Virtual Reality

Author:

Elkin Rachel L.1ORCID,Beaubien Jeff M.2,Damaghi Nathaniel3,Chang Todd P.4ORCID,Kessler David O.1

Affiliation:

1. Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA

2. Aptima, Inc., Woburn, MA, USA

3. Kalo Labs, LLC, River Edge, NJ, USA

4. Children’s Hospital Los Angeles, Los Angeles, CA, USA

Abstract

Background Recent advances in non-invasive physiologic monitoring leverage machine learning to provide unobtrusive, real-time assessments of a learner’s cognitive load (CL) as they engage in specific tasks. However, the performance characteristics of these novel composite physiologic CL measures are incompletely understood. Objectives We aimed to 1) explore the feasibility of measuring CL in real time using physiologically-derived inputs; 2) evaluate the performance characteristics of a novel composite CL measure during simulated virtual reality resuscitations; and 3) understand how this measure compares to traditional, self-reported measures of CL. Methods Novice (PGY1-2 pediatric residents) and expert (pediatric emergency medicine fellows and attendings) participants completed four virtual reality simulations as team leader. The scenario content (status epilepticus versus anaphylaxis) and level of distraction (high versus low) were manipulated. Cognitive load was measured in all participants using electroencephalography and electrocardiography data (“real-time CL”) as well as through self-report (NASA-TLX). Scenario performance also was measured. Results Complete data were available for 6 experts and 6 novices. Experts generally had lower CL than novices on both measures. Both measures localized the most significant differences between groups to the anaphylaxis scenarios (real-time CL [low-distraction] Cohen’s d -1.33 [95% CI -.2.56, -0.03] and self-reported CL [high-distraction] Cohen’s d -1.41 [95% CI -2.67, -0.10]). No consistent differences were seen with respect to level of distraction. Performance was similar between the two groups, though both exhibited fewer errors over time (F(3,48) = 5.75, p = .002). Conclusion It is feasible to unobtrusively measure cognitive load in real time during virtual reality simulations. There was convergence between the two CL measures: in both, experts had lower CL than novices, with the most significant effect size differences in the more challenging anaphylaxis scenarios.

Publisher

SAGE Publications

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3