Animating Causal Overlays

Most approaches to representing causality, such as the common causal graph, require a separate and static view, but in many cases it is useful to add the dimension of causality to the context of an existing visualization. Building on research from perceptual psychology that shows the perception of causality is a low‐level visual event derived from certain types of motion, we are investigating how to add animated causal representations, called visual causal vectors, onto other visualizations. We refer to these as causal overlays. Our initial experimental results show this approach has great potential but that extra cues are needed to elicit the perception of causality when the motions are overlaid on other graphical objects. In this paper we describe the approach and report on a study that examined two issues of this technique: how to accurately convey the causal flow and how to represent the strength of the causal effect.

[1]  Chaomei Chen,et al.  Top 10 Unsolved Information Visualization Problems , 2005, IEEE Computer Graphics and Applications.

[2]  Michael E Young,et al.  Causal impressions: Predictingwhen, not justwhether , 2005, Memory & cognition.

[3]  Colin Ware,et al.  Information Visualization: Perception for Design , 2000 .

[4]  N. Anderson,et al.  An information integration approach to phenomenal causality , 1993, Memory & cognition.

[5]  Brian J Scholl,et al.  Measuring causal perception: connections to representational momentum? , 2006, Acta psychologica.

[6]  Harold H. Chaput and Leslie B. Cohen.  A Model of infant Causal Perception and its Development , 2001 .

[7]  Timothy L. Hubbard,et al.  Naïve impetus and Michotte's "tool effect": evidence from representational momentum , 2003, Psychological research.

[8]  R. C. Oldfield.  THE PERCEPTION OF CAUSALITY , 1963 .

[9]  Prakash P. Shenoy,et al.  BAYESIAN CAUSAL MAPS AS DECISION AIDS IN VENTURE CAPITAL DECISION MAKING: METHODS AND APPLICATIONS. , 2002 .

[10]  Prakash P. Shenoy,et al.  A Bayesian network approach to making inferences in causal maps , 2001, Eur. J. Oper. Res..

[11]  Pourang Irani,et al.  Visualizing Causal Semantics Using Animations , 2007, IEEE Transactions on Visualization and Computer Graphics.

[12]  Christopher G. Healey,et al.  Visualizing data with motion , 2005, VIS 05. IEEE Visualization, 2005..

[13]  William Knight,et al.  Moving icons as a human interrupt , 1992, Int. J. Hum. Comput. Interact..

[14]  K. Nakayama,et al.  PSYCHOLOGICAL SCIENCE Research Article CAUSAL CAPTURE: Contextual Effects on the Perception of Collision Events , 2022 .

[15]  Kim Marriott,et al.  Layout of Bayesian Networks , 2005, ACSC.

[16]  K. Nakayama,et al.  Causal Capture: Contextual Effects on the Perception of Collision Events , 2002 .

[17]  Eric Neufeld,et al.  Experiments in the Perception of Causality , 2006, Smart Graphics.

[18]  Niklas Elmqvist,et al.  Animated Visualization of Causal Relations Through Growing 2D Geometry , 2004, Inf. Vis..

[19]  A. Leslie,et al.  Do six-month-old infants perceive causality? , 1987, Cognition.

[20]  Richard A. Becker,et al.  Brushing scatterplots , 1987 .

[21]  K. Nakayama,et al.  Illusory Causal Crescents: Misperceived Spatial Relations Due to Perceived Causality , 2004, Perception.

[22]  Eric Neufeld,et al.  Visualizing Causal Relations , 1999 .

[23]  A. Schlottmann,et al.  Perceived physical and social causality in animated motions: spontaneous reports and ratings. , 2006, Acta psychologica.

[24]  N. Troje,et al.  Audiovisual phenomenal causality , 2003, Perception & psychophysics.

[25]  William Knight,et al.  Simple Animation as a Human Interrupt , 1992 .

[26]  Colin Ware,et al.  Filtering and Brushing with Motion , 2002, Inf. Vis..

[27]  Timothy L. Hubbard,et al.  THE PERCEPTION OF CAUSALITY: INSIGHTS FROM MICHOTTE'S LAUNCHING EFFECT, NAÏVE IMPETUS THEORY, AND REPRESENTATIONAL MOMENTUM , 2004 .