Using Contrastive Inferences to Learn About New Words and Categories

Abstract

In the face of unfamiliar language or objects, description is one cue people can use to learn about both. Beyond narrowing potential referents to those that match a descriptor (e.g., "tall"), people could infer that a described object is one that contrasts with other relevant objects of the same type (e.g., "the tall cup" contrasts with another, shorter cup). This contrast may be in relation to other objects present in the environment (this cup is tall among present cups) or to the referent's category (this cup is tall for a cup in general). In three experiments, we investigate whether people use such contrastive inferences from description to learn new word-referent mappings and learn about new categories' feature distributions. People use contrastive inferences to guide their referent choice, though size--and not color--adjectives prompt them to consistently choose the contrastive target over alternatives (Experiment 1). People also use color and size description to infer that a novel object is atypical of its category (Experiments 2 and 3). However, these two inferences do not trade off substantially: people infer a described referent is atypical even when the descriptor was necessary to establish reference. We model these experiments in the Rational Speech Act (RSA) framework and find that it predicts both of these inferences, and a very small trade-off between them--consistent with the non-significant trade-off we observe in people's inferences. Overall, people are able to use contrastive inferences from description to resolve reference and make inferences about a novel object’s category, allowing them to learn more about new things than literal meaning alone allows.

Keywords: concept learning, contrastive inference, Word learning, Pragmatics, communication, computational modeling