High-speed visual estimation using preattentive processing

A new method is presented for performing rapid and accurate numerical estimation. The method is derived from an area of human cognitive psychology called preattentive processing. Preattentive processing refers to an initial organization of the visual field based on cognitive operations believed to b...

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Bibliographic Details
Published in:ACM transactions on computer-human interaction 1996-06, Vol.3 (2), p.107-135
Main Authors: Healey, Christopher G., Booth, Kellogg S., Enns, James T.
Format: Article
Language:English
Subjects:
Accessibility
Computer graphics
Computing methodologies
Empirical studies in HCI
Graphics input devices
Graphics systems and interfaces
Human computer interaction (HCI)
Human-centered computing
Interaction design
Interaction devices
Touch screens
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Summary:A new method is presented for performing rapid and accurate numerical estimation. The method is derived from an area of human cognitive psychology called preattentive processing. Preattentive processing refers to an initial organization of the visual field based on cognitive operations believed to be rapid, automatic, and spatially parallel. Examples of visual features that can be detected in this way include hue, intensity, orientation, size, and motion. We beleive that studies from preattentive vision should be used to assist in the design of visualization tools, especially those for which high-speed target detection, boundary identification, and region detection are important. In our present study, we investigated two known preattentive features (hue and orientation) in the context of a new task (numerical estimation) in order to see whether preattentive estimation was possible. Our experiments tested displays that were designed to visualize data from salmon migration simulations. The results showed that rapid and accurate estimation was indeed possible using either hue or orientation. Furthermore, random variation in one of these features resulted in no interference when subjects estimated the percentage of the other. To test the generality of our results, we varied two important display parameters-display duration and feature difference-and found boundary conditions for each. Implications of our results for application to real-world data and tasks are discussed.
ISSN:1073-0516
1557-7325
DOI:10.1145/230562.230563