Can Discriminative Lexicon Theory account for the family size effect in auditory word recognition?
收藏DataCite Commons2025-03-12 更新2025-04-16 收录
下载链接:
https://data.ru.nl/collections/ru/cls/discriminative_lexicon_theory_family_size_effect_auditory_word_recognition_dsc_516
下载链接
链接失效反馈官方服务:
资源简介:
Words with larger morphological families elicit shorter response times (RTs) in lexical decision experiments (e.g., Bertram, Baayen, & Schreuder, 2000). One possible account for this family size (FS) effect draws on Discriminative Lexicon Theory (Baayen et al., 2011), positing that morphological families strengthen relationships between forms and meanings. While Discriminative Lexicon Theory successfully explains FS effects in reading (Mulder et al., 2014), we will investigate whether it also does in listening. We employed the computational model LDL-AURIS (Shafaei-Bajestan et al., 2023), which is based on Discriminative Lexicon Theory, and show that, while it predicts auditory lexical decision RTs collected in a large-scale Dutch lexical decision experiment (BALDEY; Ernestus & Cutler, 2015), it does only partially explain the variance in the RTs that is explained by FS. This shows that Discriminative Lexicon Theory in its current form cannot fully explain FS effects in listening. We discuss possible reasons for this finding.
This Data Sharing Collection (DSC) includes: a) LDL-AURIS-based predictions of reaction times (RTs) in BALDEY. b) A BALDEY dataset enriched with three different family size (FS) measures. c) Control variables used in the associated experiment. d) All necessary scripts to derive the above materials
Changes in this version in comparison to the first version are: a) The analysis script now includes code to recreate the figures shown in the associated article. b) Enhancements have been made to the preprocessing steps in the analysis script. c) The documentation has been updated.
Please note that the collection does not include the CELEX or CGN databases used for computing FS measures and training the LDL-AURIS model, as we do not have the license to share them. Researchers with access to CELEX and CGN can use the provided scripts to recreate the LDL-AURIS model and FS measures. Those without access will need to use the enriched materials provided in this collection.
提供机构:
Radboud University
创建时间:
2024-01-29



