Exploring Eye Tracking to Detect Cognitive Load in Complex Virtual Reality Training

TL;DR

This study explores using eye-tracking data and machine learning models to detect cognitive load in complex VR training tasks, demonstrating preliminary feasibility and paving the way for future research.

Contribution

It introduces an eye-tracking-based approach combined with machine learning to assess cognitive load during complex VR training, an area with limited prior exploration.

Findings

Eye-tracking can predict cognitive load in VR tasks

MLP and RF models show promising accuracy

Preliminary results support further investigation

Abstract

Virtual Reality (VR) has been a beneficial training tool in fields such as advanced manufacturing. However, users may experience a high cognitive load due to various factors, such as the use of VR hardware or tasks within the VR environment. Studies have shown that eye-tracking has the potential to detect cognitive load, but in the context of VR and complex spatiotemporal tasks (e.g., assembly and disassembly), it remains relatively unexplored. Here, we present an ongoing study to detect users' cognitive load using an eye-tracking-based machine learning approach. We developed a VR training system for cold spray and tested it with 22 participants, obtaining 19 valid eye-tracking datasets and NASA-TLX scores. We applied Multi-Layer Perceptron (MLP) and Random Forest (RF) models to compare the accuracy of predicting cognitive load (i.e., NASA-TLX) using pupil dilation and fixation duration. Our preliminary analysis demonstrates the feasibility of using eye tracking to detect cognitive load in complex spatiotemporal VR experiences and motivates further exploration.