Impact of Uncertainty and Diagnosticity on Classification of Multidimensional Data with Integral and Separable Displays of System Status
Abstract
Integrative, object-like displays have been advocated for presenting multi-dimensional system data. In this research, two experiments assessed the relative merits of integral and separable displays to enhance information processing ability when the identity of an instance of system data is uncertain. In each experiment, thirty subjects, equally divided into three groups, were trained to classify instances of system state into one of four state categories using a Configural display, a Bargraph display, or a Digital display. In Experiment 1, the distribution of instances from the range of possibilities within a state category were uniform; in Experiment 2, the distribution was biased toward those instances of highly uncertain state category membership. After training, subjects received extended practice classifying instances. In both experiments, uncertainty was found to have the greatest impact on the time to classify an instance of system data. In Experiment 1, the Bargraph display was consistently superior under all conditions of uncertainty. The Configural display was found to be superior to the Digital display under conditions of low uncertainty, while the Digital display was superior to the Configural display under conditions of high uncertainty. In Experiment 2, the superiority of the Bargraph display diminished, producing results equivalent to those of the Digital display; performance with the Configural display was worse than either of the other two displays. (aw)
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 1989
- Accession Number
- ADA215966
Entities
People
- Bruce G. Coury
- Margery D. Boulette
- Robert A. Smith
Organizations
- University of Massachusetts Amherst