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.

Hsieh, C., Marelli, M., Rastle, K. (2025). Compositional processing in the recognition of Chinese compounds: Behavioural and computational studies. PSYCHONOMIC BULLETIN & REVIEW [10.3758/s13423-025-02668-8].

Compositional processing in the recognition of Chinese compounds: Behavioural and computational studies

Marelli M.;
2025

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.
Articolo in rivista - Articolo scientifico
Chinese word recognition; Compositional distributional semantics; Compound processing; Meaning construction;
English
6-mar-2025
2025
open
Hsieh, C., Marelli, M., Rastle, K. (2025). Compositional processing in the recognition of Chinese compounds: Behavioural and computational studies. PSYCHONOMIC BULLETIN & REVIEW [10.3758/s13423-025-02668-8].
File in questo prodotto:
File Dimensione Formato  
Hsieh-2025-Psychonomic Bulletin and Review-AAM.pdf

accesso aperto

Tipologia di allegato: Author’s Accepted Manuscript, AAM (Post-print)
Licenza: Licenza open access specifica dell’editore
Dimensione 202.57 kB
Formato Adobe PDF
202.57 kB Adobe PDF Visualizza/Apri
Hsieh-2025-Psychonomic Bulletin and Review-VoR.pdf

accesso aperto

Tipologia di allegato: Publisher’s Version (Version of Record, VoR)
Licenza: Creative Commons
Dimensione 810.92 kB
Formato Adobe PDF
810.92 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/546974
Citazioni
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
Social impact