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Effects of 2D geometric transformations on visual memory

Published: 28 July 2006 Publication History

Abstract

In order to provide well-grounded guidelines for interface design, we systematically examined the effects of 2D geometric transformations and background grids on visual memory. We studied scaling, rotation, rectangular fisheye, and polar fisheye transformations. Based on response time and accuracy results, we found a no-cost zone for each transformation type within which performance is unaffected. Results indicated that scaling had no effect down to at least 20% reduction. Rotation had a no-cost zone of up to 45 degrees, after which the response time increased to 5.4 s from the 3.4 s baseline without significant drop in accuracy. Interestingly, polar fisheye transformations had less effect on accuracy than their rectangular counterparts. The presence of grids extended these zones and significantly improved accuracy in all but the fisheye polar transformations. Our results therefore provided guidance on the types and levels of nonlinear transformations that could be used without affecting performance, and provided insights into the roles of transformations and grids on visual memory.

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cover image ACM Conferences
APGV '06: Proceedings of the 3rd symposium on Applied perception in graphics and visualization
July 2006
181 pages
ISBN:1595934294
DOI:10.1145/1140491
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 28 July 2006

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Author Tags

  1. geometric transformations
  2. information visualization
  3. visual memory

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  • (2021)The State of the Art in Empirical User Evaluation of Graph VisualizationsIEEE Access10.1109/ACCESS.2020.30476169(4173-4198)Online publication date: 2021
  • (2013)Many people, many eyesCHI '13 Extended Abstracts on Human Factors in Computing Systems10.1145/2468356.2479671(3299-3302)Online publication date: 27-Apr-2013
  • (2013)ViSizerIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2012.11419:2(278-290)Online publication date: 1-Feb-2013
  • (2013)The "Map" in the mental mapInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2013.08.00471:11(1044-1055)Online publication date: 1-Nov-2013
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  • (2012)The mental map and memorability in dynamic graphsProceedings of the 2012 IEEE Pacific Visualization Symposium10.1109/PacificVis.2012.6183578(89-96)Online publication date: 28-Feb-2012
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