Pragmatic theories of utterance interpretation share the assumption that listeners reason about alternative utterances that a speaker could have produced, but didn't. For such reasoning to be successful, listeners must have precise expectations about a speaker's production choices. This is at odds with the considerable variability across speakers that exists at all levels of linguistic representation. This tension can be reconciled by listeners adapting to the statistics of individual speakers. While linguistic adaptation is increasingly widely attested, semantic/pragmatic adaptation is underexplored. Moreover, what kind of representations listeners update during semantic/pragmatic adaptation – estimates of the speaker's lexicon, or estimates of the speaker's utterance preferences – remains poorly understood. In this work, we investigate semantic/pragmatic adaptation in the domain of uncertainty expressions like might and probably. In a series of web-based experiments, we find 1) that listeners vary in their expectations about a generic speaker's use of uncertainty expressions; 2) that listeners rapidly update their expectations about the use of uncertainty expressions after brief exposure to a speaker with a specific usage of uncertainty expressions; and 3) that listeners' interpretations of uncertainty expressions change after being exposed to a specific speaker. We present a novel computational model of semantic/pragmatic adaptation based on Bayesian belief updating and show, through a series of model comparisons, that semantic/pragmatic adaptation is best captured by listeners updating their beliefs both about the speaker's lexicon and their utterance preferences. This work has implications for both semantic theories of uncertainty expressions and psycholinguistic theories of adaptation: it highlights the need for dynamic semantic representations and suggests that listeners integrate their general linguistic knowledge with speaker-specific experiences to arrive at more precise interpretations.

话语解释的语用理论共有这样一个假设，即听众对说话者可能会产生但没有的其他话语进行推理。为了使这种推理成功，听众必须对演讲者的制作选择有确切的期望。这与所有语言表达水平上存在的说话人之间的巨大差异是矛盾的。听众可以根据个人说话者的统计数据来调和这种紧张感。尽管越来越多地证明了语言适应性，但对语义/语用适应性的探索还不足。此外，在语义/语用适应过程中，听众会更新哪种表示形式（说话者词典的估计或说话者话语偏好的估计）仍然知之甚少。在这项工作中可能而且可能。在一系列基于网络的实验中，我们发现：1）听众对通用说话者使用不确定性表达的期望有所不同；2）聆听者短暂接触不确定性表达的特定用法后，听众可以迅速更新对不确定性表达的期望；和3）听众对不确定性表达的理解在与特定说话者接触后会发生变化。我们提出了一种基于贝叶斯信念更新的新型语义/语用顺应性计算模型，并通过一系列模型比较表明，听众更新其对说话者词典和话语偏好的信念，可以最好地捕获语义/语用顺应性。