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Evaluation of the Index of Cognitive Activity (ICA) as an Instrument to Measure Cognitive Workload Under Differing Light Conditions

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Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018) (IEA 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 827))

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Abstract

A straightforward and valid instrument for measuring cognitive workload would be heavily appreciated in many research areas, such as human-machine-interaction, driver behavior (e.g. automation and fatigue), usability and UI design (e.g. adaptive displays), training and education, or other areas, that are interested in the assessment of the cognitive state of a person. The Index of Cognitive Activity (ICA) is a promising but also controversially discussed instrument that could be of high relevance if it keeps its promises. The ICA is a patent from the year 2000, which claims to be an effective, light-independent recording method of mental workload.

On the basis of a literature research, we carried out a lab experiment to evaluate the ICA. Participants were equipped with an Eyetracking device and worked on a mental rotation task and a Stroop task under varying light conditions. The NASA-TLX was to be answered after each test condition to evaluate the subjective workload of the participants in each condition. If the ICA is truly light-independent, the ICA should show the same mental workload for each light condition. Results show expected ICA values for the Spatial Task, but inconclusive ICA values for the Stroop Task. Possible explanations and future work is discussed.

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Correspondence to Lisa Rerhaye .

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Rerhaye, L., Blaser, T., Alexander, T. (2019). Evaluation of the Index of Cognitive Activity (ICA) as an Instrument to Measure Cognitive Workload Under Differing Light Conditions. In: Bagnara, S., Tartaglia, R., Albolino, S., Alexander, T., Fujita, Y. (eds) Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018). IEA 2018. Advances in Intelligent Systems and Computing, vol 827. Springer, Cham. https://doi.org/10.1007/978-3-319-96059-3_38

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