Early Language Learning via Spreading Activation and Category Exploration in Complex Networks
This work provides a computational model for understanding vocabulary development in children, offering insights into the interplay between activation dynamics and lexical category constraints.
The authors model early language learning as a search on a graph-based mental lexicon using spreading activation and category exploration, finding that this approach outperforms a shortest path baseline in simulating normative word acquisition across four languages, with better capture of empirical exploration dynamics.
Is word acquisition in children uneven with respect to semantic and lexical categories? To answer this question, we model early language learning as a search on a graph-based mental lexicon, driven by two interacting processes: spreading activation and an enforced exploration (rather than exploitation) of lexical categories. We evaluate model performance on four languages (German, English, Dutch, and Rioplatense Spanish), using CDIs as ground-truth data for lexical categories, normative ages derived from the Wordbank repository, and state-of-the-art resources for reconstructing graphs of word similarities. We find that spreading activation outperforms a shortest path baseline in simulating normative word acquisition. At the category level, we highlight complex transitions between CDIs. By studying their sequences in terms of burstiness and average persistence time within the same CDI, we find that spreading activation better captures the exploration dynamics observed empirically. Overall, our findings suggest that vocabulary development can be understood through the non-trivial interplay between activation dynamics and some degree of constraints regulating the visiting of lexical categories in complex networks.