Abstract
A writing system is a graphic code, i.e., a system of standardized pairings between symbols and meanings in which symbols take the form of images that can endure. The visual character of writing implies that written characters have to fit constraints of the human visual system. One aspect of this optimization lays in the graphic complexity of the characters used by scripts. Scripts are sets of graphic characters used for the written form of one language or more. Using computational methods over a large and diverse dataset (over 47,000 characters, from over 133 scripts), we answer three central questions about the visual complexity of written characters and the evolution of writing: (1) What determines character complexity? (2) Can we find traces of evolutionary change in character complexity? (3) Is complexity distributed in a way that makes character recognition easier? Our study suggests that (1) character complexity depends primarily on which linguistic unit the characters encode, and that (2) there is little evidence of evolutionary change in character complexity. Additionally (3) for an individual character, the half which is encountered first while reading tends to be more complex than that which is encountered last.
Highlights
Writing is a graphic code, i.e., a system of standardized pairings between symbols and meanings in which symbols take the form of im ages that can endure (Morin, Kelly, & Winters, 2020)
The characters of scripts are anisotropic with respect to the orientation of strokes within letters, and of mirror symmetries, two properties that can be predicted on neuroscientific grounds (Morin, 2018)
We found the predicted relationship between graph inventory size and character complexity: characters are more complex in large scripts that include numerous characters
Summary
Writing is a graphic code, i.e., a system of standardized pairings between symbols and meanings in which symbols take the form of im ages that can endure (Morin, Kelly, & Winters, 2020). The characters of scripts are anisotropic with respect to the orientation of strokes within letters (preference for vertical and horizontal strokes over obliques), and of mirror symmetries (vertical symmetry being preferred to horizontal symmetry), two properties that can be predicted on neuroscientific grounds (Morin, 2018) They tend to mimic natural scene statistics, by extensively using basic topological shapes that recur in the natural visual environment (Changizi, Zhang, Ye, & Shimojo, 2006; Testolin, Stoianov, & Zorzi, 2017), and cardinal orientations, which are over represented in the natural world (Morin, 2018). Both characteristics effectively reduce the cost of their processing by the human visual
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