eCite Digital Repository

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]


Preview
PDF
Restricted - Request a copy
316Kb
  

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

Repository Staff Only: item control page