Suppes Patrick, Perreau-Guimaraes Marcos, Wong Dik Kin
Center for the Study of Language and Information, Stanford University, Stanford, CA 94305-4101, USA.
Neural Comput. 2009 Nov;21(11):3228-69. doi: 10.1162/neco.2009.04-08-764.
The idea of a hierarchical structure of language constituents of phonemes, syllables, words, and sentences is robust and widely accepted. Empirical similarity differences at every level of this hierarchy have been analyzed in the form of confusion matrices for many years. By normalizing such data so that differences are represented by conditional probabilities, semiorders of similarity differences can be constructed. The intersection of two such orderings is an invariant partial ordering with respect to the two given orders. These invariant partial orderings, especially between perceptual and brain representations, but also for comparison of brain images of words generated by auditory or visual presentations, are the focus of this letter. Data from four experiments are analyzed, with some success in finding conceptually significant invariants.
语言成分(音素、音节、单词和句子)的层次结构这一概念稳固且被广泛接受。多年来,人们以混淆矩阵的形式分析了这个层次结构中每个层面的经验性相似性差异。通过对这些数据进行归一化处理,使差异由条件概率表示,就可以构建相似性差异的半序。两个这样的排序的交集是相对于两个给定顺序的不变偏序。这些不变偏序,特别是在感知和大脑表征之间的,也用于比较由听觉或视觉呈现生成的单词的脑图像,是这封信的重点。分析了来自四个实验的数据,在找到概念上有意义的不变量方面取得了一些成功。