Spoken communication occurs in a “noisy channel” characterized by high levels of environmental noise, variability within and between speakers, and lexical and syntactic ambiguity. Given these properties of the received linguistic input, robust spoken word recognition—and language processing more generally—relies heavily on listeners' prior knowledge to evaluate whether candidate interpretations of that input are more or less likely. Here we compare several broad-coverage probabilistic generative language models in their ability to capture human linguistic expectations. Serial reproduction, an experimental paradigm where spoken utterances are reproduced by successive participants similar to the children's game of “Telephone,” is used to elicit a sample that reflects the linguistic expectations of English-speaking adults. When we evaluate a suite of probabilistic generative language models against the yielded chains of utterances, we find that those models that make use of abstract representations of preceding linguistic context (i.e., phrase structure) best predict the changes made by people in the course of serial reproduction. A logistic regression model predicting which words in an utterance are most likely to be lost or changed in the course of spoken transmission corroborates this result. We interpret these findings in light of research highlighting the interaction of memory-based constraints and representations in language processing.

口头交流发生在“嘈杂的通道”中，其特征是高水平的环境噪声，说话者内部和之间的变异性以及词汇和句法歧义。考虑到接收到的语言输入的这些属性，健壮的口语单词识别以及更广泛的语言处理在很大程度上取决于听众的先验知识，以评估该输入的候选解释或多或少的可能性。在这里，我们比较了几种广泛的概率生成语言模型在捕获人类语言期望方面的能力。连续再现是一种实验范式，通过连续的参与者再现话语，类似于儿童的“电话”游戏，它被用来得出一个样本，该样本反映了英语成年人的语言期望。（词组结构）可以最好地预测人们在连续复制过程中所做的更改。逻辑回归模型预测其在话语中的话是最有可能会丢失或更改口语传播的过程中证实了这一结果。我们根据研究重点解释基于记忆的约束和表示在语言处理中的相互作用的研究结果。