构建了迄今最大的德语自由联想数据集，助力语言与认知研究。
The "Small World of Words" German Free-Association Norms
- 基于5877个德语提示词收集自由联想数据，流程标准化。
- 数据能有效预测词汇判断、语义相关性等心理语言学任务表现。
- 适合语言学、心理学及跨文化研究者使用。
自由联想规范为认知科学中语言、语义与文化现象的研究提供关键实证数据。尽管英语、荷兰语、西班牙语和普通话已有大规模规范数据，但德语尚无类似资源。为此，我们作为多语言小世界词汇（SWOW）项目的一部分，发布了包含5,877个德语提示词的自由联想规范数据集（SWOW-DE）。文中描述了数据采集流程、参与者特征及全面的预处理方法，并展示了结果。通过三个经典心理语言学范式验证，SWOW-DE在词汇判断、语义相关性判断和词频评级任务中均表现出稳健预测能力。此外，其响应表现优于现有德语资源，并初步揭示了跨语言共性与语言特异性联想模式，指明未来研究方向。总体而言，SWOW-DE是目前最大规模的德语自由联想数据集，为语言学、心理学及跨文化研究提供了独特资源。
原文摘要 · Abstract (English)
Free-association norms provide essential empirical data for investigating linguistic, semantic, and cultural phenomena in the cognitive sciences. Although large-scale norms exist for languages such as English, Dutch, Spanish, and Mandarin Chinese, no comparable resource has been available for German. To address this gap, we present free-association norms for 5,877 German cue words as part of the German version of the multilingual Small World of Words (SWOW) project. We describe the data collection procedures, participant characteristics, and our comprehensive preprocessing pipeline before introducing the resulting SWOW-DE data set. Using data from three established psycholinguistic paradigms, we show that SWOW-DE norms robustly predict performance in lexical decision tasks, relatedness judgments, and psycholinguistic word ratings. Furthermore, we demonstrate that SWOW-DE responses compare favorably with existing German resources and provide a preliminary cross-linguistic comparison revealing both shared and language-specific association patterns, highlighting promising directions for future research. Overall, SWOW-DE represents the largest collection of German free associations to date and offers a unique resource for linguistic, psychological, and cross-cultural research.
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