Experiments in Linguistic Meaning

We show that when analyzing data from inference judgment tasks, it can be important to incorporate into one's data analysis regime an explicit representation of the semantics of the natural language prompt used to guide participants on the task. To demonstrate this, we conduct two experiments within an existing experimental paradigm focused on measuring factive inferences, while manipulating the prompt participants receive in small but semantically potent ways. In statistical model comparisons couched within the framework of probabilistic dynamic semantics, we find that probabilistic models structured, in part, by the semantics of the prompt fit better to data collected using that prompt than models that ignore the semantics of the prompt.