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Adopting Stimulus Detection Tasks for Cognitive Workload Assessment: Some Considerations

Overview
Journal Hum Factors
Specialty Psychology
Date 2024 Jan 22
PMID 38247319
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Abstract

Objective: This article tackles the issue of correct data interpretation when using stimulus detection tasks for determining the operator's workload.

Background: Stimulus detection tasks are a relative simple and inexpensive means of measuring the operator's state. While stimulus detection tasks may be better geared to measure conditions of high workload, adopting this approach for the assessment of low workload may be more problematic.

Method: This mini-review details the use of common stimulus detection tasks and their contributions to the Human Factors practice. It also borrows from the conceptual framework of the inverted-U shape model to discuss the issue of data interpretation.

Results: The evidence being discussed here highlights a clear limitation of stimulus detection task paradigms.

Conclusion: There is an inherent risk in using a unidimensional tool like stimulus detection tasks as the primary source of information for determining the operator's psychophysiological state.

Application: Two recommendations are put forward to Human Factors researchers and practitioners dealing with the interpretation conundrum of dealing with stimulus detection tasks.

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References
1.
Morris D, Pilcher J, Switzer III F . Lane heading difference: An innovative model for drowsy driving detection using retrospective analysis around curves. Accid Anal Prev. 2015; 80:117-24. DOI: 10.1016/j.aap.2015.04.007. View

2.
Fekedulegn D, Burchfiel C, Ma C, Andrew M, Hartley T, Charles L . Fatigue and on-duty injury among police officers: The BCOPS study. J Safety Res. 2017; 60:43-51. PMC: 6311701. DOI: 10.1016/j.jsr.2016.11.006. View

3.
Snider J, Spence R, Engler A, Moran R, Hacker S, Chukoskie L . Distraction "Hangover": Characterization of the Delayed Return to Baseline Driving Risk After Distracting Behaviors. Hum Factors. 2021; 65(2):306-320. DOI: 10.1177/00187208211012218. View

4.
Strayer D, Cooper J, Turrill J, Coleman J, Hopman R . The smartphone and the driver's cognitive workload: A comparison of Apple, Google, and Microsoft's intelligent personal assistants. Can J Exp Psychol. 2017; 71(2):93-110. DOI: 10.1037/cep0000104. View

5.
Monk C, Sall R, Lester B, Stephen Higgins J . Visual and cognitive demands of manual and voice-based driving mode implementations on smartphones. Accid Anal Prev. 2023; 187:107033. DOI: 10.1016/j.aap.2023.107033. View