Department of Linguistics and Germanic, Slavic, Asian and African Languages, Michigan State University.
Cogn Sci. 2006 Jul 8;30(4):643-72. doi: 10.1207/s15516709cog0000_64.
A word-by-word human sentence processing complexity metric is presented. This metric formalizes the intuition that comprehenders have more trouble on words contributing larger amounts of information about the syntactic structure of the sentence as a whole. The formalization is in terms of the conditional entropy of grammatical continuations, given the words that have been heard so far. To calculate the predictions of this metric, Wilson and Carroll's (1954) original entropy reduction idea is extended to infinite languages. This is demonstrated with a mildly context-sensitive language that includes relative clauses formed on a variety of grammatical relations across the Accessibility Hierarchy of Keenan and Comrie (1977). Predictions are derived that correlate significantly with repetition accuracy results obtained in a sentence-memory experiment (Keenan & Hawkins, 1987).
提出了一种逐字的人类句子处理复杂度度量。该度量形式化了这样一种直觉，即理解者在处理对整个句子的句法结构贡献更多信息的单词时会遇到更多困难。这种形式化是基于给定迄今为止听到的单词的语法延续的条件熵。为了计算该度量的预测值，Wilson 和 Carroll（1954）的原始熵减少思想被扩展到无限语言。这是通过使用一种轻度上下文敏感的语言来证明的，该语言包括在 Keenan 和 Comrie（1977）的可及性层次结构上的各种语法关系上形成的关系从句。得出的预测与在句子记忆实验（Keenan 和 Hawkins，1987）中获得的重复准确性结果显著相关。
