Wulff Dirk U, Mata Rui
Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany.
Center for Cognitive & Decision Sciences, University of Basel, Basel, Switzerland.
Nat Hum Behav. 2025 Mar 11. doi: 10.1038/s41562-024-02089-y.
Taxonomic incommensurability denotes the difficulty in comparing scientific theories due to different uses of concepts and operationalizations. To tackle this problem in psychology, here we use language models to obtain semantic embeddings representing psychometric items, scales and construct labels in a vector space. This approach allows us to analyse different datasets (for example, the International Personality Item Pool) spanning thousands of items and hundreds of scales and constructs and show that embeddings can be used to predict empirical relations between measures, automatically detect taxonomic fallacies and suggest more parsimonious taxonomies. These findings suggest that semantic embeddings constitute a powerful tool for tackling taxonomic incommensurability in the psychological sciences.
分类不可通约性指的是由于概念和操作化的不同使用而导致比较科学理论存在困难。为了解决心理学中的这一问题，我们在此使用语言模型来获取语义嵌入，这些嵌入在向量空间中表示心理测量项目、量表和构念标签。这种方法使我们能够分析跨越数千个项目以及数百个量表和构念的不同数据集（例如国际个性项目池），并表明嵌入可用于预测测量之间的实证关系、自动检测分类谬误以及提出更简洁的分类法。这些发现表明，语义嵌入是解决心理科学中分类不可通约性的有力工具。
