Abstract
What makes a representation pictorial? I respond to this question as a small step toward a perceptual-cognitive understanding of graphic representation properties that play important roles in the usability of information systems. Here, I focus to capabilities that play a role in whether material objects are visually processed or recognized as pictorial or symbolized representations. I distinguish pictorial and symbolized information in terms of how each makes use of “less-learned” perceptual emulation capabilities that evolved to enable reaction to real-time environmental changes, and more-learned capabilities to recognize features in order to predict and plan (“simulate”) future changes from memory traces of past percepts. Pictorial information makes use of these capabilities to cause perceptual emulation of environmental surfaces that are not part of the marked surface and are referred to here as “pictured.” Symbolized (visual) information is conceived here as visual information from a visual representation, that, through learning and recognition, causes retrieval of memory traces that serve as resources for the construction of mental simulations beyond (or other than) what is pictured. By locating information and representation at the intersection of perceiver and environment, a preliminary model to address the perplexing problem of distinguishing pictorial from symbolized representations is introduced.
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References
Barsalou, L.W.: Simulation, situated conceptualization, and prediction. Philosophical Transactions of the Royal Society B: Biological Sciences 364(1521), 1281 (2009)
Bertin, J.: Semiology of graphics: diagrams, networks, maps. University of Wisconsin Press (1983)
Gibson, J.J.: The ecological approach to the visual perception of pictures. Leonardo 11(3), 227–235 (1978)
Goodale, M.A., Króliczak, G., Westwood, D.A.: Dual routes to action: contributions of the dorsal and ventral streams to adaptive behavior. Progress in Brain Research 149, 269–283 (2005)
Kosslyn, S.M., Thompson, W.L., Ganis, G.: The case for mental imagery, vol. 39. Oxford University Press, USA (2006)
McCloud, S.: Understanding comics: The invisible art. HarperPerennial (1993)
Moody, D.: Theory development in visual language research: Beyond the cognitive dimensions of notations. In: Proceedings of the 2009 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), pp. 151–154. IEEE Computer Society (2009)
Ramadas, J.: Visual and spatial modes in science learning. International Journal of Science Education 31(3), 301–318 (2009)
Tufte, E.R., Robins, D.: Visual explanations, vol. 25. Graphics Press, New York (1997)
Varela, F.J., Thompson, E., Rosch, E.: The embodied mind: Cognitive science and human experience. MIT press (1999)
Zuk, T., Schlesier, L., Neumann, P., Hancock, M.S., Carpendale, S.: Heuristics for information visualization evaluation. In: Proceedings of the 2006 AVI Workshop on BEyond Time and Errors: Novel Evaluation Methods for Information Visualization, pp. 1–6. ACM (2006)
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Coppin, P.W. (2012). Pictures Are Visually Processed; Symbols Are also Recognized. In: Cox, P., Plimmer, B., Rodgers, P. (eds) Diagrammatic Representation and Inference. Diagrams 2012. Lecture Notes in Computer Science(), vol 7352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31223-6_43
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DOI: https://doi.org/10.1007/978-3-642-31223-6_43
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