The Not-so-Staggering Effect of Staggered Animated Transitions on Visual Tracking

Interactive visual applications often rely on animation to transition from one display state to another. There are multiple animation techniques to choose from, and it is not always clear which should produce the best visual correspondences between display elements. One major factor is whether the animation relies on staggering-an incremental delay in start times across the moving elements. It has been suggested that staggering may reduce occlusion, while also reducing display complexity and producing less overwhelming animations, though no empirical evidence has demonstrated these advantages. Work in perceptual psychology does show that reducing occlusion, and reducing inter-object proximity (crowding) more generally, improves performance in multiple object tracking. We ran simulations confirming that staggering can in some cases reduce crowding in animated transitions involving dot clouds (as found in, e.g., animated 2D scatterplots). We empirically evaluated the effect of two staggering techniques on tracking tasks, focusing on cases that should most favour staggering. We found that introducing staggering has a negligible, or even negative, impact on multiple object tracking performance. The potential benefits of staggering may be outweighed by strong costs: a loss of common-motion grouping information about which objects travel in similar paths, and less predictability about when any specific object would begin to move. Staggering may be beneficial in some conditions, but they have yet to be demonstrated. The present results are a significant step toward a better understanding of animation pacing, and provide direction for further research.

[1]  Mercer Jennifer Ann,et al.  PUBLICATION manual of the American Psychological Association. , 1952, Psychological bulletin.

[2]  Alice M. Tybout,et al.  The Concept of External Validity , 1982 .

[3]  John Lasseter,et al.  Principles of traditional animation applied to 3D computer animation , 1987, SIGGRAPH.

[4]  Z W Pylyshyn,et al.  Tracking multiple independent targets: evidence for a parallel tracking mechanism. , 1988, Spatial vision.

[5]  Ronald M. Baecker,et al.  Animation at the interface , 1990 .

[6]  S. Yantis Multielement visual tracking: Attention and perceptual organization , 1992, Cognitive Psychology.

[7]  Bay-Wei Chang,et al.  Animation: from cartoons to the user interface , 1993, UIST '93.

[8]  Benjamin B. Bederson,et al.  Does animation help users build mental maps of spatial information? , 1999, Proceedings 1999 IEEE Symposium on Information Visualization (InfoVis'99).

[9]  Z. Pylyshyn,et al.  Tracking Multiple Items Through Occlusion: Clues to Visual Objecthood , 1999, Cognitive Psychology.

[10]  Jun Saiki,et al.  Multiple-object permanence tracking: limitation in maintenance and transformation of perceptual objects. , 2002, Progress in brain research.

[11]  Barbara Tversky,et al.  Animation: can it facilitate? , 2002, Int. J. Hum. Comput. Stud..

[12]  Catherine Plaisant,et al.  SpaceTree: supporting exploration in large node link tree, design evolution and empirical evaluation , 2002, IEEE Symposium on Information Visualization, 2002. INFOVIS 2002..

[13]  D. Pelli,et al.  Crowding is unlike ordinary masking: distinguishing feature integration from detection. , 2004, Journal of vision.

[14]  Z. Pylyshyn Some puzzling findings in multiple object tracking: I. Tracking without keeping track of object identities , 2004 .

[15]  J. Hulleman The mathematics of multiple object tracking: From proportions correct to number of objects tracked , 2005, Vision Research.

[16]  Michael S. Ambinder,et al.  Change blindness , 1997, Trends in Cognitive Sciences.

[17]  G. Cumming,et al.  Inference by eye: confidence intervals and how to read pictures of data. , 2005, The American psychologist.

[18]  Pierre Dragicevic,et al.  Les transitions visuelles différenciées: principes et applications , 2006, IHM '06.

[19]  Jeffrey Heer,et al.  Animated Transitions in Statistical Data Graphics , 2007, IEEE Transactions on Visualization and Computer Graphics.

[20]  J. Wolfe,et al.  Tracking unique objects , 2007, Perception & psychophysics.

[21]  George A Alvarez,et al.  How many objects can you track? Evidence for a resource-limited attentive tracking mechanism. , 2007, Journal of vision.

[22]  Carl Gutwin,et al.  Can smooth view transitions facilitate perceptual constancy in node-link diagrams? , 2007, GI '07.

[23]  Brian D. Fisher,et al.  Evidence against a speed limit in multiple-object tracking , 2008, Psychonomic bulletin & review.

[24]  Pierre Dragicevic,et al.  Rolling the Dice: Multidimensional Visual Exploration using Scatterplot Matrix Navigation , 2008, IEEE Transactions on Visualization and Computer Graphics.

[25]  Pourang Irani,et al.  The effect of animated transitions in zooming interfaces , 2008, AVI '08.

[26]  Elsevier Sdol International Journal of Human-Computer Studies , 2009 .

[27]  Pierre Dragicevic,et al.  Using text animated transitions to support navigation in document histories , 2010, CHI.

[28]  Pierre Dragicevic,et al.  GraphDice: A System for Exploring Multivariate Social Networks , 2010, Comput. Graph. Forum.

[29]  Jason M. Scimeca,et al.  Tracking Multiple Objects Is Limited Only by Object Spacing, Not by Speed, Time, or Capacity , 2010, Psychological science.

[30]  Pierre Dragicevic,et al.  Temporal distortion for animated transitions , 2011, CHI.

[31]  Pierre Dragicevic,et al.  Gliimpse: Animating from markup code to rendered documents and vice versa , 2011, UIST.

[32]  G. Cumming Understanding the New Statistics: Effect Sizes, Confidence Intervals, and Meta-Analysis , 2011 .

[33]  Pierre Dragicevic,et al.  Assessing the Effect of Visualizations on Bayesian Reasoning through Crowdsourcing , 2012, IEEE Transactions on Visualization and Computer Graphics.

[34]  Steven L Franconeri,et al.  A simple proximity heuristic allows tracking of multiple objects through occlusion , 2012, Attention, perception & psychophysics.

[35]  Pierre Dragicevic,et al.  Histomages: fully synchronized views for image editing , 2012, UIST '12.

[36]  S. Franconeri The Nature and Status of Visual Resources , 2013 .

[37]  Kris N Kirby,et al.  BootES: An R package for bootstrap confidence intervals on effect sizes , 2013, Behavior research methods.

[38]  P. Cavanagh,et al.  Flexible cognitive resources: competitive content maps for attention and memory , 2013, Trends in Cognitive Sciences.

[39]  Pierre Dragicevic,et al.  Running an HCI experiment in multiple parallel universes , 2014, CHI Extended Abstracts.

[40]  G. Cumming,et al.  The New Statistics , 2014, Psychological science.

[41]  Jean-Daniel Fekete,et al.  GraphDiaries: Animated Transitions andTemporal Navigation for Dynamic Networks , 2014, IEEE Transactions on Visualization and Computer Graphics.