Proceedings of the Annual Meeting of the Cognitive Science Society

A recent large-scale wug-task study found that non-nativespeakers of English tend to produce fewer regular past-tense-ed inflections than native speakers (Cuskley et al., 2015). Inthis paper we present a model that can account for this dif-ference in behaviour as resulting from a difference in inputamounts and distributions. This model attends to both fre-quency, using Bayesian non-parametric methods, and phono-logical similarity between words, using a neural model of wordforms, and unifies these factors within a single probabilisticframework. We show that the general pattern of over-use ofirregular inflections in non-native speakers can result simplyfrom exposure to a smaller amount of input and does not re-quire any model-internal distinction of native and non-nativespeakers. Our model also captures the interaction betweenclass frequency and phonological similarity that was evidentacross all participant productions.