Cross-task cue utilisation and situational awareness in learning to manage a simulated rail control task. (November 2020)

• Record Type: Journal Article
• Authors: Joffe, Anthony D.; Wiggins, Mark W.
• Is Part Of: Applied ergonomics. Volume 89(2020)
• Publication Date: 2020-11
• Subjects: Situational awareness -- SAGAT -- Cue utilisation -- Learning -- Rail control -- Simulation; Human engineering -- Periodicals; 620.82
• DOI: 10.1016/j.apergo.2020.103216
• Languages: English
• ISSNs: 0003-6870

Abstract

Abstract: Cue utilisation and situational awareness share similar properties since both constructs are dependent upon the application of feature-event associations in memory. The aim of this study was to investigate the extent to which cue utilisation and situational awareness contribute to learning a simplified, simulated rail control task incorporating an implicit pattern of train movements. Fifty-five undergraduate students completed an assessment of cue utilisation prior to completing the rail control task during periods of lower and higher task demands. Situational awareness was assessed using Situation Awareness Global Assessment Technique (SAGAT) queries. The results indicated that, while both cue utilisation and situational awareness were related to the detection of the implicit pattern of train movements, they contributed separately to performance on the simulated rail control task. The outcomes suggest that cue utilisation and situational awareness may be separate constructs, where cue utilisation constitutes a capacity variable that is associated with changes in response to task demand and situational awareness constitutes an outcome variable that emerges through task exposure. Highlights: Cue utilisation and situational awareness engage similar cognitive structures. Cue utilisation and situational awareness are associated with implicit learning. No relationship is evident between cue utilisation and situational awareness. Situational awareness predicts performance in high demand conditions. Cue utilisation predicts performance in both low and high demand conditions.