ABSTRACT
Skilled performers in complex environments rely heavily on heuristic strategies to cope with the time pressure and complexity of dynamic tasks. We suggest that the use of task simplification strategies based largely on perception and pattern recognition is fundamental to the novice-expert shift in dynamic decision making. We therefore suggest that interface training interventions should support the development of highly effective and robust heuristic strategies, rather than the development of more abstract, cognitively intensive strategies. A pair of empirical studies are presented that investigated the benefits of training interventions aimed at supporting perceptual and pattern-recognitional activities in dynamic environments. Results suggest that the acquisition of skilled performance in dynamic environments can be accelerated by supporting perceptual activities in the service of dynamic decision making. Implications of these results for training, aiding, and interface design are discussed.
Subject(s)
Cues , Decision Making , Inservice Training/methods , Pattern Recognition, Automated , Psychomotor Performance , Adult , Analysis of Variance , Female , Humans , Male , Reaction TimeABSTRACT
Task-offload aids (e.g., an autopilot, an "intelligent" assistant) can be selectively engaged by the human operator to dynamically delegate tasks to automation. Introducing such aids eliminates some task demands but creates new ones associated with programming, engaging, and disengaging the aiding device via an interface. The burdens associated with managing automation can sometimes outweigh the potential benefits of automation to improved system performance. Aid design parameters and features of the overall multitask context combine to determine whether or not a task-offload aid will effectively support the operator. A modeling and sensitivity analysis approach is presented that identifies effective strategies for human-automation interaction as a function of three task-context parameters and three aid design parameters. The analysis and modeling approaches provide resources for predicting how a well-adapted operator will use a given task-offload aid, and for specifying aid design features that ensure that automation will provide effective operator support in a multitask environment.
