Using Situation Awareness and Workload to Predict Performance in Submarine Track Management: A Multilevel Approach

Shayne Loft, Lisa Jooste, Yanqi Ryan Li, Timothy Ballard, Samuel Huf, Ottmar V. Lipp, Troy A.W. Visser

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


Objective: Examine the extent to which subjective workload and situation awareness (SA) can predict variance in performance at the between- and within-person levels of analysis in a simulated submarine track management task. Background: SA and workload are crucial constructs in human factors that are conceptualized as states that change within individuals over time. Thus, a change in an individual’s subjective workload or SA over the course of performing a task should be predictive of their subsequent performance (within-person effects). However, there is little empirical evidence for this. Method: Participants monitored displays to track the behaviors of contacts in relationship to their own ship (Ownship) and landmarks. The Situational Awareness Global Assessment Technique measured SA, and the Air Traffic Workload Input Technique measured subjective workload. Results: When a participant’s subjective workload rating increased, their subsequent performance decreased, but there was no evidence for within-person effects of SA on performance. We replicated prior between-person level effects of SA; participants with higher SA performed better than those with lower SA. Conclusion: Change in an individual’s subjective workload rating (but not SA) was predictive of their subsequent performance. Because an increase in SA should increase the extent to which operators hold the knowledge required to perform subsequent tasks, further research is required to examine SA effects on performance at the within-person level. Application: Adapting automation is more likely to produce optimal outcomes if based on measurement of operator states that predict future task performance, such as workload.

Original languageEnglish
Pages (from-to)978-991
Number of pages14
JournalHuman Factors
Issue number7
Publication statusPublished - 1 Nov 2018


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