MAASTRICHT UNIVERSITY

‍Client: Maastricht University in collaboration with various educational institutions

Use Case: Measuring Cognitive Load in Virtual Reality Training via Pupillometry

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Researchers from Maastricht University use SightLab VR Pro, the WorldViz VR Experiment Generator for Vizard, for conducting a study on the application of pupillometry to measure cognitive load in virtual reality (VR) training environments. The study aims to validate task-evoked pupillary responses (TEPRs) as reliable indicators of cognitive load during VR-based simulation training.

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"One of the challenges with performing research using virtual reality and eye tracking is the complexity of building the initial experiment. With built-in tools specifically for setting up eye tracking experiments in VR, SightLab VR got us up and running and doing our research in very little time with minimal effort!"

                                                                                                                                                                                                                Joy Yeonjoo Lee, PHD, Maastricht University

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Researchers Involved

• Joy Yeonjoo Lee
• Nynke de Jong
• Jeroen Donkers
• Halszka Jarodzka
• Jeroen J. G. van Merriënboer

The study is published in the IEEE Transactions on Learning Technologies, Volume 17, and explores the use of TEPRs to assess cognitive load in VR training scenarios, specifically in the context of home health care.

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Study Overview

• Objective: To determine the validity of TEPRs as measures of cognitive load in VR training by examining the correlation between task difficulty and TEPRs, and their relation to cognitive load self-ratings and performance.
• Participants: 14 undergraduate students in Health Sciences at Maastricht University.
• Methodology: Participants performed observation tasks in VR under two conditions—easy and difficult—while their eye movements and light reflexes were monitored using a VR headset with a built-in eye tracker and photosensor.
• The study utilized SightLab VR Pro, an experiment generator software designed to complement Vizard, a comprehensive VR software for researchers.
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On the left, a photosensor was installed inside the headset to measure the light intensity of the display. On the right, the Arduino board was mounted on the outside of the VR headset to connect the sensor to the computer via USB.

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Key Findings

• Task Difficulty and TEPRs: Difficult tasks resulted in significantly larger TEPRs compared to easy tasks, indicating higher cognitive load.
• Correlation with Performance: In difficult task conditions, TEPRs positively correlated with performance measures, suggesting TEPRs as valid indicators of cognitive load in complex VR training scenarios.
• Light Reflex Correction: By employing a baseline correction method for light reflexes, TEPRs were effectively isolated, enhancing the accuracy of cognitive load measurement in VR.

These findings demonstrate the potential of TEPRs to serve as real-time, objective measures of cognitive load in VR training environments. This opens up possibilities for improving instructional design and assessment in VR-based education, particularly in fields requiring high levels of situational and emotional management, such as healthcare training.

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Implications for VR Training

The results highlight the utility of pupillometry in providing valuable insights into cognitive processes during VR training. This can aid in the development of more effective training programs by enabling real-time monitoring and adjustment based on cognitive load.

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Future Research

The study suggests further exploration with larger sample sizes and in various domains to validate the findings. Additionally, the integration of more advanced VR functions, such as haptic interactions, could enhance the applicability of TEPRs in diverse training environments.

Link to Study: https://ieeexplore.ieee.org/abstract/document/10292541

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