Categorical Structure in Early Semantic Networks of Nouns

Josita Maouene, Grand Valley State University

Abstract

Despite what we know about children’s ability to categorize, it is not clear to what extent information in the environment is capable of facilitating higher-order category knowledge, nor to what extent different kinds of object features play different kinds of roles. As a start we built a network of 130 early-learned nouns with 1394 perceptual and functional features as given by adult judgments. Then we analyzed the basic structural properties of the network. These revealed a small world structure and a high degree of feature overlap in local clusters. To identify the local clusters, we used a clique percolation algorithm to parse the network in terms of the statistical properties of feature overlap. This enabled us to identify clusters of items with a strong resemblance to common categories, such as animals, foods, and vehicles. Perceptual and functional features were found to play different roles in the categorization, with functional information being less redundant but more specific than perceptual information.