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Cognitive workload measurement and modeling under divided attention

Citation

Castro, SC and Strayer, DL and Matzke, D and Heathcote, A, Cognitive workload measurement and modeling under divided attention, Journal of Experimental Psychology: Human Perception and Performance, 45, (6) pp. 826-839. ISSN 0096-1523 (2019) [Refereed Article]


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Copyright Statement

Copyright 2019 American Psychological Association This paper is not the copy of record and may not exactly replicate the authoritative document published in the APA journal. Please do not copy or cite without author's permission. The final article is available, upon publication, at: https://doi.org/10.1037/xhp0000638

DOI: doi:10.1037/xhp0000638

Abstract

Motorists often engage in secondary tasks unrelated to driving that increase cognitive workload, resulting in fatal crashes and injuries. An International Standards Organization method for measuring a driver's cognitive workload, the detection response task (DRT), correlates well with driving outcomes, but investigation of its putative theoretical basis in terms of finite attention capacity remains limited. We address this knowledge gap using evidence-accumulation modeling of simple and choice versions of the DRT in a driving scenario. Our experiments demonstrate how dual-task load affects the parameters of evidence-accumulation models. We found that the cognitive workload induced by a secondary task (counting backward by 3s) reduced the rate of evidence accumulation, consistent with rates being sensitive to limited-capacity attention. We also found a compensatory increase in the amount of evidence required for a response and a small speeding in the time for nondecision processes. The International Standards Organization version of the DRT was found to be most sensitive to cognitive workload. A Wald-distributed evidence-accumulation model augmented with a parameter measuring response omissions provided a parsimonious measure of the underlying causes of cognitive workload in this task. This work demonstrates that evidence-accumulation modeling can accurately represent data produced by cognitive workload measurements, reproduce the data through simulation, and provide supporting evidence for the cognitive processes underlying cognitive workload. Our results provide converging evidence that the DRT method is sensitive to dynamic fluctuations in limited-capacity attention.

Item Details

Item Type:Refereed Article
Keywords:cognitive workload, detection response task, driving simulation, evidence accumulation modeling, multitasking
Research Division:Psychology and Cognitive Sciences
Research Group:Cognitive Sciences
Research Field:Decision Making
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in Psychology and Cognitive Sciences
UTAS Author:Heathcote, A (Professor Andrew Heathcote)
ID Code:134063
Year Published:2019
Web of Science® Times Cited:2
Deposited By:Psychology
Deposited On:2019-07-24
Last Modified:2020-07-28
Downloads:0

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