Alexis Wellwood, Annie Gagliardi, Jeffrey Lidz
Acquiring the correct meanings of words expressing quantities (seven, most) and qualities (red, spotty) present a challenge to learners. Understanding how children succeed at this requires understanding, not only of what kinds of data are available to them, but also the biases and expectations they bring to the learning task. The results of our word-learning task with 4-year-olds indicate that a “syntactic bootstrapping” hypothesis correctly predicts a bias toward quantity-based interpretations when a novel word appears in the syntactic position of a determiner but also leaves open the explanation of a bias towards quality-based interpretations when the same word is presented in the syntactic position of an adjective. We develop four computational models that differentially encode how lexical, conceptual, and perceptual factors could generate the latter bias. Simulation results suggest it results from a combination of lexical bias and perceptual encoding.