A Statistical Associative Account of Vocabulary Growth in Early Word Learning

Abstract

There are an infinite number of possible word-to-world pairings. One way children could learn words at an early stage is by computing statistical regularities across different modalities—pairing spoken words with possible referents in the co-occurring extralinguistic environment, collecting a number of such pairs, and then figuring out the common elements. This paper provides computational evidence that such a statistical mechanism is possible for object name learning. Moreover, young children learn words much more effectively and efficiently at later stages. Could statistical learning account for this behavioral change? The current paper explores this question by presenting a developmental model of word learning that relies on a general associative mechanism and recruits previously learned words to guide subsequent word learning. This mechanism leads to increasingly fast learning and corresponding behavioral changes. Simulation studies are conducted using the data collected from a series of picture-book reading episodes wherein parents were asked to narrate books to their 20-month-old children. The results show that previously learned lexical knowledge can narrow the search space and therefore reduce the degree of ambiguity in word-to-world mappings. This results in the bootstrapping of lexical acquisition without changing the underlying statistical learning mechanism. Hence, this work suggests that using lexical knowledge accumulated in prior statistical learning could play an important role in vocabulary growth. To our knowledge, this is the first model that attempts to simulate the effects of cumulative knowledge on subsequent learning using realistic data collected from child-caregiver interactions.