Learning about others: Modeling social inference through ambiguity resolution.

Bayesian accounts of social cognition successfully model the human ability to infer goals and intentions of others on the basis of their behavior. In this paper, we extend this paradigm to the analysis of ambiguity resolution during brief communicative exchanges. In a reference game experimental setup, we observed that participants were able to infer listeners' preferences when analyzing their choice of object given referential ambiguity. Moreover, a subset of speakers was able to strategically choose ambiguous over unambiguous utterances in an epistemic manner, although a different group preferred unambiguous utterances. We show that a modified Rational Speech Act model well-approximates the data of both the inference of listeners' preferences and their utterance choices. In particular, the observed preference inference is modeled by Bayesian inference, which computes posteriors over hypothetical, behavior-influencing inner states of conversation partners-such as their knowledge, beliefs, intentions, or preferences-after observing their utterance-interpretation behavior. Utterance choice is modeled by anticipating social information gain, which we formalize as the expected knowledge change, when choosing a particular utterance and watching the listener's response. Taken together, our results demonstrate how social conversations allow us to (sometimes strategically) learn about each other when observing interpretations of ambiguous utterances.