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Real-time prediction of short-timescale fluctuations in cognitive workload


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Boehm, U, Matzke, D, Gretton, M, Castro, S, Cooper, J, Skinner, M, Strayer, D and Heathcote, A ORCID: 0000-0003-4324-5537 2021 , 'Real-time prediction of short-timescale fluctuations in cognitive workload' , Cognitive Research, vol. 6, no. 1 , pp. 1-29 , doi: 10.1186/s41235-021-00289-y.

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Human operators often experience large fuctuations in cognitive workload over seconds timescales that can leadto sub-optimal performance, ranging from overload to neglect. Adaptive automation could potentially address thisissue, but to do so it needs to be aware of real-time changes in operators’ spare cognitive capacity, so it can providehelp in times of peak demand and take advantage of troughs to elicit operator engagement. However, it is unclearwhether rapid changes in task demands are refected in similarly rapid fuctuations in spare capacity, and if so whataspects of responses to those demands are predictive of the current level of spare capacity. We used the ISO standarddetection response task (DRT) to measure cognitive workload approximately every 4 s in a demanding task requiringmonitoring and refueling of a feet of simulated unmanned aerial vehicles (UAVs). We showed that the DRT provideda valid measure that can detect diferences in workload due to changes in the number of UAVs. We used cross-validation to assess whether measures related to task performance immediately preceding the DRT could predict detectionperformance as a proxy for cognitive workload. Although the simple occurrence of task events had weak predictiveability, composite measures that tapped operators’ situational awareness with respect to fuel levels were much moreefective. We conclude that cognitive workload does vary rapidly as a function of recent task events, and that real-timepredictive models of operators’ cognitive workload provide a potential avenue for automation to adapt without anongoing need for intrusive workload measurements.

Item Type: Article
Authors/Creators:Boehm, U and Matzke, D and Gretton, M and Castro, S and Cooper, J and Skinner, M and Strayer, D and Heathcote, A
Keywords: cognitive workload, detection response task, cross-validation, workload prediction, human-automation teaming
Journal or Publication Title: Cognitive Research
Publisher: Springer
ISSN: 2365-7464
DOI / ID Number: 10.1186/s41235-021-00289-y
Copyright Information:

© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons Attribution 4.0 International (CC BY 4.0) licence, ( and indicate if changes were made

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