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

Citation

Boehm, U and Matzke, D and Gretton, M and Castro, S and Cooper, J and Skinner, M and Strayer, D and Heathcote, A, Real-time prediction of short-timescale fluctuations in cognitive workload, Cognitive Research, 6, (1) pp. 1-29. ISSN 2365-7464 (2021) [Refereed Article]


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Copyright Statement

© 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, (https://creativecommons.org/licenses/by/4.0/) and indicate if changes were made

DOI: doi:10.1186/s41235-021-00289-y

Abstract

Human operators often experience large fuctuations in cognitive workload over seconds timescales that can lead to sub-optimal performance, ranging from overload to neglect. Adaptive automation could potentially address this issue, but to do so it needs to be aware of real-time changes in operators’ spare cognitive capacity, so it can provide help in times of peak demand and take advantage of troughs to elicit operator engagement. However, it is unclear whether rapid changes in task demands are refected in similarly rapid fuctuations in spare capacity, and if so what aspects of responses to those demands are predictive of the current level of spare capacity. We used the ISO standard detection response task (DRT) to measure cognitive workload approximately every 4 s in a demanding task requiring monitoring and refueling of a feet of simulated unmanned aerial vehicles (UAVs). We showed that the DRT provided a 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 detection performance as a proxy for cognitive workload. Although the simple occurrence of task events had weak predictive ability, composite measures that tapped operators’ situational awareness with respect to fuel levels were much more efective. We conclude that cognitive workload does vary rapidly as a function of recent task events, and that real-time predictive models of operators’ cognitive workload provide a potential avenue for automation to adapt without an ongoing need for intrusive workload measurements.

Item Details

Item Type:Refereed Article
Keywords:cognitive workload, detection response task, cross-validation, workload prediction, human-automation teaming
Research Division:Psychology
Research Group:Cognitive and computational psychology
Research Field:Cognition
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in psychology
UTAS Author:Gretton, M (Mr Matthew Gretton)
UTAS Author:Heathcote, A (Professor Andrew Heathcote)
ID Code:146178
Year Published:2021
Web of Science® Times Cited:1
Deposited By:Pharmacy
Deposited On:2021-08-24
Last Modified:2021-09-30
Downloads:8 View Download Statistics

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