title: Reasoning in Reference Games: Individual- vs. Population-Level Probabilistic Modeling.
abstract: Recent advances in probabilistic pragmatics have achieved considerable success in modeling speakers' and listeners' pragmatic reasoning as probabilistic inference. However, these models are usually applied to population-level data, and so implicitly suggest a homogeneous population without individual differences. Here we investigate potential individual differences in Theory-of-Mind related depth of pragmatic reasoning in so-called reference games that require drawing ad hoc Quantity implicatures of varying complexity. We show by Bayesian model comparison that a model that assumes a heterogenous population is a better predictor of our data, especially for comprehension. We discuss the implications for the treatment of individual differences in probabilistic models of language use.
authors.affiliation: Department of Linguistics, University of Tübingen, Wilhelmstrasse 19, 72072 Tübingen, Germany.
authors.forename: Michael
authors.initials: M
authors.lastname: Franke
authors.affiliation: Department of Psychology, Stanford University, 450 Serra Mall, Stanford, CA 94305, United States of America.
authors.forename: Judith
authors.initials: J
authors.lastname: Degen
mesh_terms: D001499:Bayes Theorem; D032882:Comprehension; D003657:Decision Making; D005717:Games, Experimental; D006801:Humans; D015233:Models, Statistical