Development and validation of a Chinese pseudo-character/non-character producing system

Area of Science:

• Cognitive Psychology
• Linguistics
• Computational Linguistics

Background:

• Studying Chinese lexical processing requires controlled experimental materials.
• Existing tools for generating Chinese pseudo-characters are limited in scope and data-driven approaches.

Purpose of the Study:

• To develop and validate a comprehensive system for generating Chinese pseudo-characters and non-characters.
• To provide researchers with a tool to manipulate orthographic characteristics for studying Chinese reading.

Main Methods:

• Developed a Chinese pseudo-character/non-character producing system (CPN system) based on a dataset of 6097 characters.
• Extracted detailed orthographic information including radical structure, frequency, stroke count, and positional regularity.
• Validated the system's stimuli through an orthographic choice task with Chinese-as-second-language learners and native speakers.

Main Results:

• The CPN system successfully generated pseudo-characters, semi non-characters, and whole non-characters.
• Empirical validation showed that proficient readers better identify pseudo-characters, indicating the impact of radical position-based information.
• The system provides downloadable images and data for efficient stimulus creation.

Conclusions:

• The CPN system is a valuable, large-scale, data-driven tool for Chinese orthographic processing research.
• The system facilitates the study of Chinese lexical processing, instruction, and cross-linguistic comparisons.
• Radical position-based information significantly influences Chinese character identification.