Feedback design for the control of a dynamic multitasking system : dissociating outcome feedback from control feedback

Abstract

Objective:We distinguish outcome feedback from control feedback to show that sub- optimal performance in a dynamic multitasking system may be caused by limits inher- ent to the information provided rather than human resource limits.Background:Tardast is a paradigm for investigating human multitasking behavior, complex system management, and supervisory control. Prior research attributed the suboptimal perfor- mance of Tardast operators to poor strategic task management.Methods:We varied the nature of performance feedback in the Tardast paradigm to compare continuous, cumulative feedback (global feedback) on performance outcome with feedback lim- ited to the most recent system state (local feedback).Results:Participants in both con- ditions improved with practice, but those with local feedback performed better than those with global feedback. An eye gaze analysis showed increased visual attention directed toward the feedback display in the local feedback condition.Conclusion:Pre- dicting performance in the control of a dynamic multitasking system requires under- standing the interactions between embodied cognition, the task being performed, and characteristics of performance feedback. In the current case, at least part of what had been diagnosed as a deficit caused by limited cognitive resources has been shown to be data limited.Application:Perfect outcome feedback can provide inadequate control feedback. Instances of suboptimal performance can be alleviated by better feedback de- sign that takes into account the temporal dynamics of the human-system interaction.