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Invited Talk
Human Vision at a Glance (Invited Talk)

Authors: Ruth Rosenholtz and Dian Yu

Published in: LIPIcs, Volume 142, 14th International Conference on Spatial Information Theory (COSIT 2019)


Abstract
Recent advances in human vision research have pointed toward a theory that unifies many aspects of vision relevant to information visualization. According to this theory, loss of information in peripheral vision determines performance on many visual tasks. This theory subsumes old concepts such as visual saliency, selective attention, and change blindness. It predicts the rich details we have access to at a glance. Furthermore, it provides insight into tasks not commonly studied in human vision, such as ability to comprehend connections in a network diagram, or to compare information in one part of a display with that in another.

Cite as

Ruth Rosenholtz and Dian Yu. Human Vision at a Glance (Invited Talk). In 14th International Conference on Spatial Information Theory (COSIT 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 142, pp. 1:1-1:4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


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@InProceedings{rosenholtz_et_al:LIPIcs.COSIT.2019.1,
  author =	{Rosenholtz, Ruth and Yu, Dian},
  title =	{{Human Vision at a Glance}},
  booktitle =	{14th International Conference on Spatial Information Theory (COSIT 2019)},
  pages =	{1:1--1:4},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-115-3},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{142},
  editor =	{Timpf, Sabine and Schlieder, Christoph and Kattenbeck, Markus and Ludwig, Bernd and Stewart, Kathleen},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2019.1},
  URN =		{urn:nbn:de:0030-drops-110937},
  doi =		{10.4230/LIPIcs.COSIT.2019.1},
  annote =	{Keywords: human vision, information visualization, attention, eye movements, peripheral vision, gist, ensemble perception, search, saliency}
}
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