Real-time operator workload assessment and state classification may be useful for decisions about when and how to dynamically apply automation to information processing functions in aviation systems. This research examined multiple cognitive workload measures, including secondary task performance and physiological (cardiac) measures, as inputs to a neural network for operator functional state classification during a simulated air traffic control (ATC) task. Twenty-five participants performed a low-fidelity simulation under manual control or 1 of 4 different forms of automation. Traffic volume was either low (3 aircraft) or high (7 aircraft). Participants also performed a secondary (gauge) monitoring task. Results demonstrated significant effects of traffic volume (workload) on aircraft clearances (p < .01) and trajectory conflicts (p < .01), secondary task performance (p < .01), and subjective ratings of task workload (p < .01). The form of ATC automation affected the number of aircraft collisions (p < .05), secondary task performance (p < .01), and heart rate (HR; p < .01). However, heart rate and heart rate variability measures were not sensitive to the traffic manipulation. Neural network models of controller workload (defined in terms of traffic volume) were developed using the secondary task performance and simple heart rate measure as inputs. The best workload classification accuracy using a genetic algorithm (across all forms of ATC automation) was 64%,
The objective of this research was to assess the effectiveness of the Situation Awareness Global Assessment Technique (SAGAT) as an indicator of automation state changes in adaptive automation (AA) of a complex, dynamic control task. An air traffic control (ATC)-related simulation was developed to present automation of four different information processing (IP) functions, including information acquisition, information analysis, decision making, and action implementation, as well as to simulate a completely manual control condition. Eight participants operated the ATC simulation under the five conditions. SAGAT data revealed only a general effect of automated versus manual control, but no significant effects of the modes of AA on SA. These results suggested that SAGAT was not a sensitive measure in the ATC-related task. Consequently, a modified SAGAT measure is proposed with relevance weighting of environmental stimuli to promote sensitivity and reliability of measurement of SA in the target domain.
The objective of this study was to assess the use of a computational cognitive model for describing human performance with an adaptively automated system, with and without advance cueing of control mode transitions. A dual-task piloting simulation was developed to collect human performance data under auditory cueing or no cueing of automated or manual control. GOMSL models for simulating user behavior were constructed based on a theory of increased memory transactions at mode transitions. The models were applied to the same task simulation and scenarios performed by the humans. Comparison of results on human and model output demonstrated the model to be generally descriptive of performance; however, it was not accurate in predicting timing of memory use in preparing for manual control. Interestingly, the human data didn't reveal differences between cued and no cue trials. A refined GOMSL model was developed by modifying assumptions on the timing and manner of memory use, and considering human parallel processing in dual-task performance. Results revealed the refined model to be more plausible for representing behavior. Computational cognitive modeling appears to be a viable approach to represent operator performance in adaptive systems.
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