Abstract

A crucial component of language acquisition involves organizing words into grammatical categories and discovering relations between them. Many studies have argued that phonological or semantic cues or multiple correlated cues are required for learning. Here we examine how distributional variables will shift learners from forming a category of lexical items to maintaining lexical specificity. In a series of artificial language learning experiments, we vary a number of distributional variables to category structure and test how adult learners use this information to inform their hypotheses about categorization. Our results show that learners are sensitive to the contexts in which each word occurs, the overlap in contexts across words, the non-overlap of contexts (or systematic gaps), and the size of the data set. These variables taken together determine whether learners fully generalize or preserve lexical specificity.

Introduction

Language acquisition crucially involves finding the grammatical categories of words in the input. The organization of elements into categories, and the generalization of patterns from some seen element combinations to novel ones, account for important aspects of the expansion of linguistic knowledge in early stages of language acquisition. One hypothesis of how learners approach the problem of categorization is that the categories (but not their contents) are innately specified prior to experiencing any linguistic input, with the assignment of tokens to categories accomplished with minimal exposure. A second possibility is that the categories are formed around a semantic definition. A third hypothesis, explored in the present research, is that the distributional information in the environment is sufficient (along with a set of learning biases) to extract the categorical structure of natural language. While it is likely that each of these sources of evidence makes important contributions to language acquisition, this third hypothesis regarding distributional learning has often been thought to be an unlikely contributor, given the information processing limitations of young children and the complexity of the computational processes that would be entailed.