{"title":"Compositional processing in the recognition of Chinese compounds: Behavioural and computational studies.","authors":"Cheng-Yu Hsieh, Marco Marelli, Kathleen Rastle","doi":"10.3758/s13423-025-02668-8","DOIUrl":null,"url":null,"abstract":"<p><p>Recent research has shown that the compositional meaning of a compound is routinely constructed by combining meanings of constituents. However, this body of research has focused primarily on Germanic languages. It remains unclear whether this same computational process is also observed in Chinese, a writing system characterised by less systematicity of the meanings and functions of constituents across compounds. We quantified the ease of integrating the meanings of Chinese constituent characters into a compositional compound meaning using a computational model based on distributional semantics. We then showed that this metric predicted sensibility judgements on novel compounds (Study 1), lexical decision latencies for rejecting novel compounds (Study 2), and lexical decision latencies for recognising existing compounds (Study 3). These results suggest that a compositional process is involved in Chinese compound processing, even in tasks that do not explicitly require meaning combination. Our results also suggest that a generic statistical learning framework is able to capture the meaningful functions of Chinese compound constituents. We conclude by discussing the advantages of routine meaning construction during compound processing in Chinese reading.</p>","PeriodicalId":20763,"journal":{"name":"Psychonomic Bulletin & Review","volume":" ","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Psychonomic Bulletin & Review","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.3758/s13423-025-02668-8","RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0
Abstract
Recent research has shown that the compositional meaning of a compound is routinely constructed by combining meanings of constituents. However, this body of research has focused primarily on Germanic languages. It remains unclear whether this same computational process is also observed in Chinese, a writing system characterised by less systematicity of the meanings and functions of constituents across compounds. We quantified the ease of integrating the meanings of Chinese constituent characters into a compositional compound meaning using a computational model based on distributional semantics. We then showed that this metric predicted sensibility judgements on novel compounds (Study 1), lexical decision latencies for rejecting novel compounds (Study 2), and lexical decision latencies for recognising existing compounds (Study 3). These results suggest that a compositional process is involved in Chinese compound processing, even in tasks that do not explicitly require meaning combination. Our results also suggest that a generic statistical learning framework is able to capture the meaningful functions of Chinese compound constituents. We conclude by discussing the advantages of routine meaning construction during compound processing in Chinese reading.
期刊介绍:
The journal provides coverage spanning a broad spectrum of topics in all areas of experimental psychology. The journal is primarily dedicated to the publication of theory and review articles and brief reports of outstanding experimental work. Areas of coverage include cognitive psychology broadly construed, including but not limited to action, perception, & attention, language, learning & memory, reasoning & decision making, and social cognition. We welcome submissions that approach these issues from a variety of perspectives such as behavioral measurements, comparative psychology, development, evolutionary psychology, genetics, neuroscience, and quantitative/computational modeling. We particularly encourage integrative research that crosses traditional content and methodological boundaries.