EXPERIMENTS IN DISPLAY EVALUATION.

Abstract

Four experiments were performed with the following objectives: (1) further explore a proposed method of display evaluation; (2) relate the proposed method to conventional measures of display 'goodness'; (3) examine the effects of three types of informational irrelevancy on decision adequacy; and (4) test a hypothesis relating to the effects of compressing discrete informational units onto single symbols. In all experiments except those related to objective 2, the subject's task was to make a military-type decision based on information presented on the display. Two decision-game formats were used; the first was logistical while the second used an air-reconnaissance situation. In these studies, each decision the subject made was associated with an explicit cost-payoff function. Since each problem required a series of such decisions, a weighted sum of the payoffs, minus the costs, was the method of display evaluation. Each of several display variables (such as density, use of color, or clutter), and response variables (such as search time) was related to cost-payoff scores, depending on the experiment. Major conclusions were that the decision-quality metric is potentially useful for evaluating display systems, and that information-extraction measures apparently are unrelated to the metric. It was also revealed that the effectiveness of a display is positively related to the amount and randomness of irrelevant information presented in the display. (Author)

Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1967
Accession Number
AD0658733

Entities

People

  • Carl A. Silver
  • Daniel Landis
  • James M. Jones
  • Robert M. Slivka

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Display Systems
  • Extraction
  • Reconnaissance
  • Test And Evaluation

Readers

  • Brain and Cognitive Science; Experimental Psychology; Cognitive Neuroscience
  • Computational Modeling and Simulation
  • Human-Computer Interaction (HCI).

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference