Understanding Cognitive Strategy WithAdaptive Automation in Dual-Task Performance Using Computational Cognitive Models

摘要

The objectives of this study were to investigate the effects of advance auditory cuing of control mode changes in an adaptively automated system on human performance and to explain cognitive behaviors at mode changes by using a computational cognitive model. A dual-task piloting simulation, involving tracking and tactical decision making, was developed to collect human performance data with auditory cuing or no cuing of mode transitions in the tactical task. Computational GOMS (goal, operators, methods, and selection) language models were coded to simulate user behavior on the basis of expectation of increased memory transactions (between long-term and working stores) at mode transitions. The models were applied to the same task simulation and scenarios performed by the humans. Human performance data did not reveal differences between cued and no-cue trials possibly because of distraction from the tracking (secondary loading) task. Comparison of results for human and model output demonstrated the model to be descriptive of the pattern of human performance across conditions but not accurate in predicting timing of memory use in preparing for manual control. A refined GOMS language model was coded on the basis of a modified assumption that memory stores are used on an ad hoc basis after high-workload mode transitions and with consideration of human parallel processing in dual-task performance. Results revealed the refined model to have greater plausibility for representing actual behavior. The manner of operator use of memory stores for controlling an adaptive system provides insight into the impact of cuing of mode transitions and a basis for future systems design.