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Predicting graph reading performance: A cognitive approach
Citation
Huang, W and Hong, S-H and Eades, P, Predicting graph reading performance: A cognitive approach, Conferences in Research and Practice in Information Technology, February 2006, Tokyo, Japan, pp. 1-10. (2006) [Refereed Conference Paper]
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Copyright Statement
Copyright 2006, Australian Computer Society, Inc. This pa- per appeared at Asia-Paci畚 Symposium on Information Visu- alization (APVIS 2006), Tokyo, Japan, February 2006. Confer- ences in Research and Practice in Information Technology, Vol. 60. K. Misue, K. Sugiyama and J. Tanaka, Ed. Reproduction for academic, not-for pro眩 purposes permitted provided this text is included.
Official URL: https://www.acs.org.au/
Abstract
Performance and preference measures are commonly
used in the assessment of visualization techniques.
This is important and useful in understanding differences in e容ctiveness between di容rent treatments.
However, these measures do not answer how and why
the di容rences are caused. And sometimes, performance measures alone may not be sensitive enough
to detect di容rences. In this paper, we introduce a
cognitive approach for visualization e容ctiveness and
e帷iency assessment. A model of user performance,
mental e峨rt and cognitive load (memory demand) is
proposed and further mental e峨rt and visualization
e帷iency measures are incorporated into our analysis. It is argued that 1) combining cognitive measures
with traditional methods provides us new insights and
practical guidance in visualization assessment. 2) analyzing human cognitive process not only helps to understand how viewers interact with visualizations, but
also helps to predict user performance in initial stage.
3) keeping cognitive load induced by a visualization
low allows more memory resources to be available for
high level complex cognitive activities. A case study
conducted supports our arguments.
Item Details
| Item Type: | Refereed Conference Paper |
|---|---|
| Keywords: | graph reading, cognitive load, mental ef- fort, cognitive model, visualization e帷iency, social network. |
| Research Division: | Information and Computing Sciences |
| Research Group: | Information Systems |
| Research Field: | Computer-Human Interaction |
| Objective Division: | Expanding Knowledge |
| Objective Group: | Expanding Knowledge |
| Objective Field: | Expanding Knowledge in the Information and Computing Sciences |
| Author: | Huang, W (Dr Tony Huang) |
| ID Code: | 90189 |
| Year Published: | 2006 |
| Deposited By: | Computing and Information Systems |
| Deposited On: | 2014-03-27 |
| Last Modified: | 2014-08-06 |
| Downloads: | 0 |
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