Reasoning in Reference Games: Individual- vs. Population-Level Probabilistic Modeling

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...

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Bibliographic Details
Published in:PloS one 2016-05, Vol.11 (5), p.e0154854-e0154854
Main Authors: Franke, Michael, Degen, Judith
Format: Article
Language:English
Subjects:
Analysis
Bayes Theorem
Bayesian analysis
Biology and Life Sciences
Cognition & reasoning
Comprehension
Decision Making
Engineering and Technology
Experimental psychology
Fingers & toes
Games
Games, Experimental
Humans
Language
Mathematical models
Medicine and Health Sciences
Memory
Modelling
Models, Statistical
Population
Probabilistic inference
Probabilistic models
Quantitative psychology
Reasoning
Social Sciences
Theory
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0154854