Visual complexity in orthographic learning: Modeling learning across writing system variations

ABSTRACT

The visual complexity of orthographies varies across writing systems. Prior research has shown that complexity strongly influences the initial stage of reading development: the perceptual learning of grapheme forms. This study presents a computational simulation that examines the degree to which visual complexity leads to grapheme learning difficulty. We trained each of 131 identical neural networks to learn the structure of a different orthography and demonstrated a strong, positive association between network learning difficulty and multiple dimensions of grapheme complexity. We also tested the model’s performance against grapheme complexity effects on behavioral same/different judgments. Although the model was broadly consistent with human performance in how processing difficulty depended on the complexity of the tested orthography, as well as its relationship to viewers’ first-language orthography, discrepancies provided insight into important limitations of the model. We discuss how visual complexity can be a factor leading to reading difficulty across writing systems.

Supplemental Material

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Additional information

Funding

This work was funded by a grant from the National Science Foundation (NSF) award SBE-0836012 through LearnLab, a grant from the NSF award BCS-1354350 to D. Plaut and M. Behrmann, and a grant from the Ministry of Education and the Ministry of Science and Technology in Taiwan (MOST 104-2911-I-003-301). The first author is indebted to Joseph Stafura and Xiaoping Fang for their helpful discussions during preparing the first version of the manuscript. The authors thank the anonymous reviewers and the editors for their comments and suggestions.