Yoshihara Masahiro, Itaguchi Yoshihiro
Graduate School of International Cultural Studies, Tohoku University, Sendai, Japan.
Department of Psychology, Keio University, 2-15-45, Mita, Minato-ku, Tokyo, 108-8345, Japan.
Behav Res Methods. 2025 Jun 30;57(8):209. doi: 10.3758/s13428-025-02696-1.
In the semantic variant of verbal fluency tests (VFTs), clustering analysis has become popular for examining the semantic structure. While the computational psycholinguistics approach has recently drawn attention to increasing the reproducibility of clustering analysis, such an approach is not available in all languages. To make the computational approach available in the Japanese language, we constructed a Japanese latent Dirichlet allocation (LDA) model. Our LDA model enables researchers and clinicians to objectively quantify the associative relationships of words, thereby making it possible to automatically detect semantic clusters. We conducted the semantic VFT with healthy young Japanese adults to examine the validity of our LDA model. We performed clustering analyses using the computational approach with our LDA model and the conventional manual approach with human coders. The results showed that the LDA model identified semantic clusters, as did the human coders. In addition, we demonstrated for the first time that response intervals within a cluster were significantly shorter than those outside of clusters, regardless of the clustering approaches. This indicates that both approaches reflect a broadly accepted assumption that closer semantic relations require less processing time. However, LDA-based clustering produced, on average, larger clusters than human-based clustering did, indicating that the LDA model captured semantic relationships between words that human coders would not recognize. Taken together, the present results demonstrated the validity of our LDA model. We hope that our LDA model fosters the use of the computational linguistic approach in semantic VFTs with Japanese participants.
在言语流畅性测试（VFTs）的语义变体中，聚类分析已成为检验语义结构的常用方法。虽然计算心理语言学方法最近已引起人们对提高聚类分析可重复性的关注，但这种方法并非适用于所有语言。为了使计算方法能够应用于日语，我们构建了一个日语潜在狄利克雷分配（LDA）模型。我们的LDA模型使研究人员和临床医生能够客观地量化词语之间的联想关系，从而能够自动检测语义聚类。我们对健康的日本年轻成年人进行了语义VFT，以检验我们LDA模型的有效性。我们使用我们的LDA模型的计算方法和人工编码员的传统手动方法进行聚类分析。结果表明，LDA模型识别出了语义聚类，人工编码员也是如此。此外，我们首次证明，无论采用何种聚类方法，聚类内的反应间隔都显著短于聚类外的反应间隔。这表明这两种方法都反映了一个广泛接受的假设，即语义关系越紧密，处理时间越短。然而，基于LDA的聚类平均产生的聚类比基于人工的聚类更大，这表明LDA模型捕捉到了人工编码员无法识别的词语之间的语义关系。综上所述，目前的结果证明了我们LDA模型的有效性。我们希望我们的LDA模型能促进在有日本参与者的语义VFT中使用计算语言学方法。
