Choosing Specifiers: An Evaluation of the Basic Tasks Model of Graphical Perception

Abstract

Effect sizes obtained from 39 experiments were used to evaluate the predictions of the basic tasks model of graphical efficacy. This model predicts that performance will be attenuated with graphical displays as a function of the particular specifier, or visual dimension, used to code data values. In this review the basic tasks model predicted performance more accurately than did Tufte's data-ink principle. In addition, variability in effect sizes across studies revealed that the model was more successful at predicting performance in local (focusing) tasks than in global information synthesis tasks. Furthermore, the model was better at predicting performance in tasks requiring the use of physically present rather than remembered graphs. Further differences in effect sizes resulted from variability in the exact specifiers used in experimental graphs. Minimal differences were obtained among graphs that used position, length, or angle as specifiers. However, graphs that used area or volume to represent quantitative values were associated with consistently worse performance than found with other formats.

Bar and Line Graph Comprehension: An Interaction of Top‐Down and Bottom‐Up Processes

“…Cleveland & McGill (Cleveland, 1993;Cleveland & McGill, 1985) provide an excellent analysis of the relative accuracy in using different visual dimensions (e.g., color, shading, area, volume, and so forth) to display quantity, which has been further modified by others (Carswell, 1992;Simkin & Hastie, 1986;Spence & Lewandowsky, 1991). We argue that similar analyses need to be conducted on visual dimensions that may or may not affect visual chunking during graph comprehension.…”

“…Viewers are faster at reading individual data points when viewing bar graphs compared to line graphs, and they are faster at making trend judgments when viewing line graphs compared to bar graphs (Simcox, 1984). Likewise, viewers can more accurately identify individual data points from bar graphs than from line graphs (Carswell & Wickens, 1987;Carswell, 1992). Furthermore, individuals are more likely to spontaneously make discrete comparisons (i.e., x 1 is greater than x 2 ) when viewing data in bar graphs and more likely to describe trends (i.e., as x increases y increases) when viewing line graphs in open-ended description tasks (Carswell, Emery, & Lonon, 1993;Zacks & Tversky, 1999).…”

Thinking Graphically: Connecting Vision and Cognition during Graph Comprehension

“…For example, Carswell (1992) evaluated the predictions of a basic task model of graphical perception, Ratwani,Trafton, primarily based on Cleveland and McGill (1984), and found task models were more successful at accounting for specific extraction tasks than integration tasks. In the last 15 years, there have been three notable theories that go beyond pure information extraction to integration.…”

A Comparative Study of Three Sorting Techniques in Performing Cognitive Tasks on a Tabular Representation

“…Wittenburg et al (2001) only reported participants' subjective ratings, so it is difficult to know whether Parallel Bargrams actually improved decision quality, or if it was highly rated because of novelty effects. The evaluation should be designed to capture if the visualization is actually influencing the information process of individuals (Carswell, 1992). Pirolli and Rao (1996) used the GOMS method, which is based on predefined sets of tasks, but it is difficult to say that these predefined tasks are what people do in realistic situa-tions.…”