Situation Awareness, Mental Workload, and Trust in Automation: Viable, Empirically Supported Cognitive Engineering Constructs

Cognitive engineering needs viable constructs and principles to promote better understanding and prediction of human performance in complex systems. Three human cognition and performance constructs that have been the subjects of much attention in research and practice over the past three decades are...

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Veröffentlicht in:Journal of cognitive engineering and decision making 2008-06, Vol.2 (2), p.140-160
Hauptverfasser: Parasuraman, Raja, Sheridan, Thomas B., Wickens, Christopher D.
Format: Artikel
Sprache:eng
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Zusammenfassung:Cognitive engineering needs viable constructs and principles to promote better understanding and prediction of human performance in complex systems. Three human cognition and performance constructs that have been the subjects of much attention in research and practice over the past three decades are situation awareness (SA), mental workload, and trust in automation. Recently, Dekker and Woods (2002) and Dekker and Hollnagel (2004; henceforth DWH) argued that these constructs represent “folk models” without strong empirical foundations and lacking scientific status. We counter this view by presenting a brief description of the large science base of empirical studies on these constructs. We show that the constructs can be operationalized using behavioral, physiological, and subjective measures, supplemented by computational modeling, but that the constructs are also distinct from human performance. DWH also caricatured as “abracadabra” a framework suggested by us to address the problem of the design of automated systems (Parasuraman, Sheridan, & Wickens, 2000). We point to several factual and conceptual errors in their description of our approach. Finally, we rebut DWH's view that SA, mental workload, and trust represent folk concepts that are not falsifiable. We conclude that SA, mental workload, and trust are viable constructs that are valuable in understanding and predicting human-system performance in complex systems.
ISSN:1555-3434
2169-5032
DOI:10.1518/155534308X284417