Using the words toddlers know now to predict the words they will learn next

Using the words toddlers know now to predict the words they will learn next Nicole M. Beckage (Nicole.Beckage@Colorado.edu) Department of Psychology and Neuroscience, 345 UCB Boulder, CO 80309 USA Eliana Colunga (Eliana.Colunga@Colorado.edu) Department of Psychology and Neuroscience, 345 UCB Boulder, CO 80309 USA is, we ask: can we use the words a child knows now to predict the words that a child will learn next? Abstract We set forth to show that lexical connectivity plays a role in understanding early word learning. By considering words that are learned in temporal proximity to one another to be related, we are able to better predict the words next learned by toddlers. We build conditional probability models based on data from the growing vocabularies of 77 toddlers, followed longitudinally for a year. This type of conditional probability model outperforms the current norms based on baseline probabilities of learning given age alone. This is a first step to capturing the interaction between a child’s productive vocabulary and their learning environment in order to understand what words a child might learn next. We also test different types of variants of this conditional probability and find that not only is there information in words that are learned in proximity to one another but that it matters how models integrate this information. The application of this work may provide better cognitive models of acquisition and perhaps allow us to detect children at risk for enduring language difficulties earlier and more accurately. Measuring the developing lexicon One well-established way to characterize toddlers’ lexicons is to use vocabulary checklists, such as the MacArthur-Bates Communicative Development Inventory (CDI; Fenson et al., 1994). These parent-reported measures have been shown to be effective in evaluating children’s communicative skills up to 30 months of age (e.g., Thal et al., 1999). The CDI: Words & Sentences Toddler form is a checklist of over 700 early words that at least 50% of children typically say at 30 months of age. By pooling data over thousands of children, the CDI provides norms of the percentage of children who say each of these words at a given age from 16 to 30 months of age, month by month. Aside from being shown to be a valid measure of communicative skills for this age group, the CDI has been recently shown to be an effective tool for sorting toddlers at the low and high end of the acquisition distribution into late talkers and typically developing children (Heilmann, et. al, 2005). This might allow us to see universality in learning but it also masks some of how the process works—the aggregate cannot explain individual differences but models of learning necessarily must. The CDI norms can be used to build models of growth. For example, Hills and colleagues (2009) have used CDI norms to build growth models based on networks of words connected by feature similarity or associative strength. Beckage, Smith and Hills (2011) used the connectivity of language within the vocabulary of young learners and showed that there are differences in the structure of the vocabularies of children at risk for language impairments and those of typically developing children. Note that these approaches presuppose that there is information in the relationships between words. If this is the case, there should be predictive power in looking at the between-word dependencies over time. We do this by exploiting the statistical regularities present in the developing vocabularies of 77 children, followed longitudinally for a year, at monthly intervals. Keywords: word learning, semantics, language acquisition, co-occurrence, development, longitudinal data, CDI Introduction Do children learn words systematically? There is a lot of evidence that words are not all learned equally. Perhaps not surprisingly, for example, parents’ vocabulary is related to their children’s vocabulary (e.g., Weizman & Snow, 2001; Veen, et al., 2009). That is, the child will learn the words in his or her environment. In addition, some concepts, and therefore the words that name them, may be easier to learn than others. For example, concrete nouns are learned earlier than verbs and adjectives (e.g., Sandhofer, Smith, & Luo, 2000; Gentner, 2006). Furthermore, the child may bring some preferences and constraints to the task of word learning. For example, children may become particularly interested in dinosaurs or construction equipment or even tea sets (DeLoache, Simcock, & Macari, 2007). That is, in characterizing the forces that guide word learning, there is evidence that at least three distinct but not necessarily mutually exclusive sources of information can come to bear: a) the structure and composition of the linguistic environment, b) the structure of the concepts and categories being named, and c) the characteristics of the learner itself. In this paper we focus mostly on this third source of variability by constructing conditional probability models from longitudinal trajectories of word learning that make predictions at the word level, for individual children. That Rationale We propose a simple way of uncovering the interaction between the language environment and learning and thus uncovering more of the systematicity of word learning. Instead of just considering the frequency of production for a