Graphic complexity in writing systems

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.1. Introduction 1.1. Lexicon 1.2. What determines the graphic complexity of a script's characters: Type, size, phylogeny? 1.2.1. Size hypothesis: Scripts with larger graph inventories have more complex symbols 1.2.2. Homogeneity hypothesis: Most variance in character complexity is captured at the level of the script 1.3. The cultural evolution of writing: Do characters become less complex over time? 1.3.1. Invention hypothesis: Recently invented scripts are more complex than more ancient scripts 1.3.2. Descendants hypothesis: Parent scripts have more complex characters compared to their offspring 1.4. Order hypothesis: The distribution of complexity inside characters follows writing and reading direction 2. Methods 2.1. Pre-registration and data accessibility 2.2. Inventory constitution 2.2.1. Script-level inclusion rules 2.2.2. Character-level inclusion 2.2.3. Description of the dataset 2.2.4. Dataset restrictions for the distribution of complexity inside characters 2.3. Measures of visual complexity 2.3.1. Perimetric complexity 2.3.2. Algorithmic complexity 2.4. Pictures processing 2.4.1. Generating pictures of characters 2.4.2. Resizing 2.4.3. Homogenizing line thickness 2.4.4. Additional treatment for algorithmic complexity 2.4.5. Pictures of characters' vertical halves 2.5. Phylogeny, size, type, and other information 2.5.1. Sources 2.5.2. Graph inventory size 2.5.3. Script classification: Families 2.5.4. Types of writing systems 2.5.5. Idiosyncratic scripts 2.5.6. Direction of writing 3. Results 3.1. Size hypothesis 3.2. Homogeneity hypothesis 3.3. Invention hypothesis 3.4. Descendants hypothesis 3.4.1. Prediction 1: First halves are more complex than last halves 3.4.2. Prediction 2: Complexity differentials between character halves depend on order (first vs. last), more than laterality (left vs. right) 3.4.3. Post hoc test, controlling for laterality biases 4. Discussion 4.1. Importance of writing system type and inventory graph size for character complexity 4.2. No decrease in character complexity 4.3. The distribution of visual complexity inside characters reflects script directionality 4.4. Limitations and future direction