[POSTER] Double Reality: Shifting the Gaze Between the Physical Object and Its Digital Representation

Lucignano, Lorenzo; Dillenbourg, Pierre

doi:10.1109/ismar-adjunct.2017.50

Cited by 1 publication

(3 citation statements)

References 21 publications

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“…Average fixation duration may be a more sensitive parameter than others, or the indication behind the differences in three fixation parameters may need further clarification, including the potential differentiation between engagement from working memory overload (Lucignano & Dillenbourg, 2017; Wang et al, 2018).…”

Section: Resultsmentioning

confidence: 99%

“…Lucignano and Dillenbourg took fixation count and fixation duration as indicators of cognitive load to test the construction structure learning system with an AR tablet display (Lucignano & Dillenbourg, 2017). They compared the ways that participants look at the physical models of structure and its augmentation on a tablet computer when the tablet was held in hands and when it was kept on a support.…”

Section: Fixationmentioning

confidence: 99%

“…parameters may need further clarification, including the potential differentiation between engagement from working memory overload(Lucignano & Dillenbourg, 2017;Wang et al, 2018).…”

mentioning

confidence: 99%

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

Suzuki,

Wild,

Scanlon

2023

Computer Assisted Learning

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BackgroundCognitive 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.ObjectivesThe 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.MethodsThis 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.ResultsPhysiological 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.ConclusionsA 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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Section: Resultsmentioning

confidence: 99%

Section: Fixationmentioning

confidence: 99%