Measuring cognitive load in augmented reality with physiological methods: A systematic review

Suzuki, Yuko; Wild, Fridolin and Scanlon, Eileen (2024). Measuring cognitive load in augmented reality with physiological methods: A systematic review. Journal of Computer Assisted Learning, 40(2) pp. 375–393.



Background: Cognitive load during AR use has been measured conventionally by performance tests and subjective rating. With the growing interest in physiological measurement using non‐invasive biometric sensors, unbiased real‐time detection of cognitive load in AR is expected. However, a range of sensors and parameters are used in various subject fields, and reported results are fragmented.
Objectives: The aim of this review is to analyse systematically how physiological methods have been used to measure cognitive load and what the implications are for the future research on AR‐based tools.
Methods: This paper took the systematic review approach. Through screening with 10 exclusion criteria, 23 studies, that contain 3 key elements: AR‐based intervention, cognitive state examination and physiological methods, were identified, analysed and synthesised. Results: Physiological methods in their current form require reference to provide meaningful interpretations and suggestions. Therefore, they are often combined with conventional methods. Many studies investigate the effect of wearable devices in comparison with non‐AR stimuli, which has been controversial, but detection of different causes of cognitive load are on the horizon. Eye‐tracking is the method most used and most consistent in the use of its parameters.
Conclusions: A multi‐method approach combining two or more evaluation instruments is essential for the validation of users' cognitive state. In addition to the AR stimuli in question, having another independent variable such as task difficulty in experiment design is useful. Statistical approaches with more data input could help establish a reliable scale. The future research should attempt to dissociate cognitive load caused by different effects such as device, instruction, and other AR techniques as well as intrinsic and extraneous aspects, in a better experimental setup with multiple parameters.

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