Effect of Uncertainty and diagnosticity on Classification of Multidimensional Data with Integral and Separable Displays of System Status

Abstract

Integrative, objectlike displays have been advocated for presenting multidimensional system data. In this research two experiments assess the effect of uncertainty on the processing of integral and separable displays. In each experiment 30 subjects were trained to classify instances of system state into one of four state categories using a configural display, a bar graph display, or a digital display. In Experiment 1 the range of instances from the state categories was uniform; in Experiment 2 the distribution was biased toward those instances of highly uncertain state category membership. After training, subjects received extended practice classifying system data. In both experiments uncertainty was found to have the greatest effect on classification performance. In Experiment 1 the bar graph display was consistently superior; the configural display was superior to the digital display only under conditions of low uncertainty. In Experiment 2 the superiority of the bar graph display diminished, producing results equivalent to those of the digital display, with the configural display producing the worst performance. The effect of uncertainty on classification performance is discussed, with specific attention paid to the apparent configural and separable properties of the bar graph display.