Behavior Research Methods

, Volume 50, Issue 1, pp 427–449 | Cite as

GraphCom: A multidimensional measure of graphic complexity applied to 131 written languages

Article

Abstract

We report a new multidimensional measure of visual complexity (GraphCom) that captures variability in the complexity of graphs within and across writing systems. We applied the measure to 131 written languages, allowing comparisons of complexity and providing a basis for empirical testing of GraphCom. The measure includes four dimensions whose value in capturing the different visual properties of graphs had been demonstrated in prior reading research—(1) perimetric complexity, sensitive to the ratio of a written form to its surrounding white space (Pelli, Burns, Farell, & Moore-Page, 2006); (2) number of disconnected components, sensitive to discontinuity (Gibson, 1969); (3) number of connected points, sensitive to continuity (Lanthier, Risko, Stolz, & Besner, 2009); and (4) number of simple features, sensitive to the strokes that compose graphs (Wu, Zhou, & Shu, 1999). In our analysis of the complexity of 21,550 graphs, we (a) determined the complexity variation across writing systems along each dimension, (b) examined the relationships among complexity patterns within and across writing systems, and (c) compared the dimensions in their abilities to differentiate the graphs from different writing systems, in order to predict human perceptual judgments (n = 180) of graphs with varying complexity. The results from the computational and experimental comparisons showed that GraphCom provides a measure of graphic complexity that exceeds previous measures in its empirical validation. The measure can be universally applied across writing systems, providing a research tool for studies of reading and writing.

Keywords

Connected points Disconnected component Gestalt principles Graphic complexity Perimetric complexity Simple features Writing systems 

Notes

Author note

This work was supported by the National Science Foundation (Grant #SBE-0836012) through Pittsburgh Science of Learning Center (PSLC) and “Aim for the Top University Project” of the National Taiwan Normal University and the Ministry of Education, Taiwan, R.O.C. The authors thank Adrian Maries and members in the Perfetti Lab at the University of Pittsburgh for their assistance with various tasks, and all observers for their participation. Moreover, the authors acknowledge the insightful comments of David Share and other, anonymous reviewers.

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Copyright information

© Psychonomic Society, Inc. 2017

Authors and Affiliations

  1. 1.Department of Applied Chinese Language and CultureNational Taiwan Normal UniversityTaipeiTaiwan
  2. 2.Department of StatisticsUniversity of WashingtonSeattleUSA
  3. 3.Learning Research and Development CenterUniversity of PittsburghPittsburghUSA

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