Probing the Mental Models of System State Categories with Multidimensional Scaling
Abstract
Identification and recognition of system state is fundamental to the effective supervision of a complex system. In supervisory tasks where control decisions are based on multidimensional data and information, the operator's ability to map the values of critical system variables to known definitions of system state is the prerequisite step to selecting the best course of action. Identifying the underlying decision criteria used by operators to classify system state and revealing the way in which that information is internally represented by individual operators is one of the major challenges facing designers of decision aids for process plants. This research describes the use of multidimensional scaling (MDS) to probe the structure and composition of the metal models of used operators to identify system state, and evaluate the impact of different display representations on those models. Twenty subjects were trained to classify instances of system data. Pairwise similarity ratings of instances of system data were analyzed by MDS to reveal the dominant dimensions used by operators. Results showed that significant individual differences emerged, and that the dimensions used by subjects were also a function of the type of display representation. (AW)
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 01, 1987
- Accession Number
- ADA215967
Entities
People
- Bruce G. Coury
- Monica C. Zubritzky
- V. G. Cuqlock-knopp
Organizations
- University of Massachusetts Amherst