Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
J Int Neuropsychol Soc. 2012 May;18(3):565-75. doi: 10.1017/S1355617712000136. Epub 2012 Mar 6.
Decreased productivity on verbal fluency tasks by persons with schizophrenia has been attributed to semantic system abnormalities. Semantic structure is often assessed using multidimensional scaling (MDS) to detect normal and aberrant semantic clustering. However, MDS has limitations that may be particularly problematic for such assessments. Here, we introduce a different clustering technique, singular value decomposition (SVD), to elucidate abnormalities of the semantic system in schizophrenia. We compared 102 treated outpatients with schizophrenia to 109 healthy adults on two category-cued word fluency tasks. Patients with schizophrenia showed semantic clustering patterns that differ markedly from those of healthy adults. However, SVD revealed more detailed and critical semantic system abnormalities than previously appreciated using MDS. Patients with schizophrenia showed less coherent semantic clustering of both low- and high-frequency category exemplars than healthy adults. These results suggest the intriguing possibility that impaired automatic activation of semantic information is a key deficit in schizophrenia.
精神分裂症患者在词语流畅性任务上的生产力下降归因于语义系统异常。语义结构通常使用多维标度（MDS）进行评估，以检测正常和异常的语义聚类。然而，MDS 存在一些限制，这些限制可能对这些评估特别成问题。在这里，我们引入了一种不同的聚类技术，奇异值分解（SVD），以阐明精神分裂症中语义系统的异常。我们比较了 102 名接受治疗的精神分裂症门诊患者和 109 名健康成年人在两个类别提示词流畅性任务上的表现。精神分裂症患者的语义聚类模式与健康成年人明显不同。然而，SVD 揭示了比以前使用 MDS 更详细和更关键的语义系统异常。精神分裂症患者的低频和高频类别示例的语义聚类一致性较差。这些结果表明，语义信息自动激活受损可能是精神分裂症的一个关键缺陷。
