Interaction of Word Learning and Semantic Category Formation in Late Talking

Interaction of Word Learning and Semantic Category Formation in Late Talking Aida Nematzadeh , Afsaneh Fazly , and Suzanne Stevenson Department of Computer Science University of Toronto {aida,afsaneh,suzanne}@cs.toronto. edu Abstract Late talkers (LTs) — children who show a marked delay in vocabulary learning — have also been shown to differ from normally-developing (ND) children with respect to the seman- tic organization of their learned vocabulary. We use a compu- tational model of word learning to study how individual dif- ferences between LTs and NDs give rise to differences in ab- stract knowledge of categories emerging from learned words, and how this affects their subsequent word learning. Our re- sults suggest that the vocabulary composition of LTs and NDs differ at least partially due to a deficit in the attentional abili- ties of LTs, which also results in the learning of weaker abstract knowledge of semantic categories of words. Introduction Late talkers (LTs) are children with a marked delay in word learning at an early age, some of whom go on to exhibit spe- cific language impairment (SLI). Early identification of LTs at risk for SLI is especially important, since early intervention can produce significant changes in the language development of these children (Desmarais, Sylvestre, Meyer, Bairati, & Rouleau, 2008). Many psycholinguistic studies have thus fo- cused on understanding signs of late talking, as well as factors contributing to it (Paul & Elwood, 1991; Thal, Bates, Good- man, & Jahn-Samilo, 1997; Rescorla & Merrin, 1998; Ellis Weismer & Evans, 2002; Rowe, 2008; Stokes & Klee, 2009). An important observation about late-talking children is that they seem to not only learn more slowly than their normally- developing (ND) peers, but also to learn differently. For example, the vocabulary composition of LTs shows greater variability, e.g., in terms of how consistently certain proper- ties, such as shape, are associated with particular categories, such as solid objects (Jones & Smith, 2005; Colunga & Sims, 2011). More generally, the vocabulary of LTs has been shown to exhibit less semantic connectivity than that of NDs (Sheng & McGregor, 2010; Beckage, Smith, & Hills, 2010). The greater variability and the weaker connectivity in the vocab- ulary of LTs call for further investigation since they might be reflective of underlying cognitive deficits in these children. Psycholinguistic evidence suggests that children’s word learning improves when they form some abstract knowledge about what kinds of semantic properties are relevant to what kinds of categories (Jones, Smith, & Landau, 1991; Colunga & Smith, 2005; Colunga & Sims, 2011). This abstract knowl- edge is argued to emerge by generalizing over the learned words. Stated otherwise, words that have been learned con- tribute to generalized abstract knowledge about word mean- ings and semantic categories, which then guide subsequent word learning. It is possible that because of the differences in the vocabulary composition of LTs and NDs, the two groups of children also form different abstract knowledge of cate- gories, which causes differences in their word learning (as suggested by Jones & Smith, 2005; Colunga & Sims, 2011). We investigate this possibility by examining within a com- putational model the precise interaction between early word learning and knowledge of semantic categories of words. We do so by extending an existing model of cross-situational word learning (Fazly, Alishahi, & Stevenson, 2010). As in Nematzadeh, Fazly, and Stevenson (2011), we simulate the difference between ND and LT learners as a difference in the ability of the cross-situational learning mechanism to at- tend to appropriate semantic features for a word. Within this framework, we propose a new model that forms clusters of words according to their learned semantic properties, and that uses this knowledge in guiding the future associations be- tween words and meanings. We show that the semantic clus- ters of words are qualitatively very different for our ND and LT models; moreover, the two learners exhibit striking differ- ences in terms of the usefulness of their learned clusters for subsequent word learning. Through computational modeling, we thus suggest an interaction between the impaired ability of LTs to form informative abstract semantic categories, and the observed delay in their vocabulary acquisition. The Computational Model Overview of the Word Learning Model The model of Fazly et al. (2010) is a cross-situational learner that incrementally forms probabilistic associations between words and their semantic properties. The input to a child is simulated as a sequence of utterances (a set of words), each paired with a scene representation (a set of semantic features, representing what is perceived when the words are heard): Utterance: { she, drinks, milk } Scene: { ANIMATE , PERSON , FEMALE , CONSUME , DRINK , SUBSTANCE , FOOD , DAIRY - PRODUCT } Given such an input pair, the model adjusts its probabilis- tic representation of the meaning of each word. First, the model determines, based on its current probabilistic knowl- edge of word–meaning associations, which semantic features in the scene are more and less likely to be associated with each word in the utterance. Using that assessment of word– feature alignment in the current input, the model then updates its probabilistic representation of the meaning of each word. In this way, the model uses cross-situational evidence to gradually improve its representation of the meaning of each word w as a probability distribution, p(.|w), over all semantic features: i.e., p( f |w) is the probability of feature f being part