Does Statistical Word Learning Scale? It's a Matter of Perspective

Mutual Exclusivity and Vocabulary Structure Daniel Yurovsky Ricardo A. H. Bion dyurovsk@indiana.edu Department of Psychological and Brain Sciences Indiana University ricardoh@stanford.edu Department of Psychology Stanford University Linda B. Smith Anne Fernald smith4@indiana.edu Department of Psychological and Brain Sciences Indiana University afernald@stanford.edu Department of Psychology Stanford University Abstract The words that children learn can be characterized as a semantic network, with links connecting related words. Recent analyses have shown these networks to have small- world structure, with a few highly-connected hub words facilitating short paths between otherwise distant words. This structure contributes to network robustness, and differences in structure can predict differences in language learning outcomes. While previous studies have shown that semantic network structure reflects linguistic input structure, we provide the first evidence that it is related also to children’s own language learning biases. Two-year old children who show a mutual-exclusivity bias have significantly more hub- like networks than children who do not, even when they know the same number of words. This finding contributes to our understanding of both semantic networks and the origins of mutual exclusivity. Keywords: word learning; mutual exclusivity; semantic networks; language acquisition Introduction Although the earliest analyses of human memory and learning concerned the learning of lists of unrelated words (Ebbinghaus, 1885/1962), researchers quickly discovered that the words people learn in more natural contexts are intricately connected. Vocabularies were conceptualized as richly structured networks, with links connecting semantically related words (Collins & Loftus, 1975). These connections play an important role in both learning and memory, and can be observed empirically in semantic priming experiments. Because activation spreads from words to their semantic neighbors, presenting a word, even subliminally, leads to faster processing of related words (Anderson, 1983). Even two-year old infants show semantic priming, suggesting that vocabularies have network structure early in language learning (Arias-Trejo & Plunkett, Recently, the application of graph-theoretic methods to the study of these networks has begun to provide insight into their structural properties. For instance, Hills, Maouene, Maouene, Sheya, & Smith (2009a) analyzed the semantic network structure of 130 nouns typically learned before 30 months. Compared to randomly-connected control networks, these semantic networks showed significant small-world structure, in which most words are sparsely connected, but a few are highly-connected hubs. This kind of structure results in networks robust to malfunction (e.g. forgetting a word; Albert, Jeong, & Barabasi, 2000), and can help to explain some of the remarkable efficiency of human semantic memory (Raaijmakers & Shiffrin, 1981). Further, semantic networks lacking this structure are associated with slower language-learning, characterizing the vocabulary structure of late talkers (Beckage, Smith, & Hills, 2011). But why do children learn these words? Why do semantic networks have this structure? Undoubtedly, one answer to this question is that structure comes from the environment. Because children learn words from the language they hear, language input is a strong predictor of the words that children will learn. For instance, the frequency with which a child hears a word in isolation can predict how likely a child is to learn that word (Brent & Siskind, 2001). Similarly, the semantic networks constructed from corpora of both adult-directed and child- directed language have many of the same structural properties as networks constructed from the words 30- month-old children are likely to know (Hills, et al., 2009a; Steyvers & Tenenbaum, 2005). But perhaps a more complete explanation of the origin of semantic network structure is that it emerges from an interaction between structure in the linguistic environment and the child’s own learning system. Because children are not unbiased samplers of linguistic input, their attentional and learning biases mediate the link between language input and language learned (Hudson Kam & Newport, 2005; Smith, 2000). For instance, children who learn to attend to shape are likely to learn shape-based categories, and those who learn to attend to other properties (e.g. material) learn other kinds of words (Colunga & Sims, 2011; Smith, Jones, Landau, Gershkoff-Stowe, & Samuelson, 2002). Can word- learning biases predict and explain semantic network structure? In this paper, we consider the case of disambiguation through mutual exclusivity. In the disambiguation task, a child is presented with a novel object among one or more familiar object competitors. The child then hears a novel label (e.g. ‘can you find the dax? ’) and is asked to select an object. Both toddlers and adults reliably select the novel object as the target of the novel label (Markman & Wachtel, 1988; Golinkoff, Hirsh- Pasek, Bailey, & Wenger, 1992), and studies with infants suggest that this disambiguation may arise as early as 18 to 22.5 months (Halberda, 2003; Mather & Plunkett, 2009). Preferential