Clare E. Sims

Interactions in the development of skilled word learning in neural networks and toddlers

Development is about change over time. Computational models have provided insights into the developmental changes seen in different cognitive phenomena, including within the domain of word learning. The present paper uses a computational model in tandem with a behavioral study to make and test predictions about the interdependencies between the emergences of different word learning biases. The model is used to investigate how the shape bias influences novel noun generalization to other types of items, and to guide a behavioral study of this effect in children. The results provide a novel view of biased word learning over time, and suggest that emerging biases interact with each other and influence how networks and children attend to different kinds of information over time.

Beyond modeling abstractions: learning nouns over developmental time in atypical populations and individuals.

Connectionist models that capture developmental change over time have much to offer in the field of language development research. Several models in the literature have made good contact with developmental data, effectively captured behavioral tasks, and accurately represented linguistic input available to young children. However, fewer models of language development have truly captured the process of developmental change over time. In this review paper, we discuss several prominent connectionist models of early word learning, focusing on semantic development, as well as our recent work modeling the emergence of word learning biases in different populations. We also discuss the potential of these kinds of models to capture children’s language development at the individual level. We argue that a modeling approach that truly captures change over time has the potential to inform theory, guide research, and lead to innovations in early language intervention.