title: Categorical structure among shared features in networks of early-learned nouns.
abstract: The shared features that characterize the noun categories that young children learn first are a formative basis of the human category system. To investigate the potential categorical information contained in the features of early-learned nouns, we examine the graph-theoretic properties of noun-feature networks. The networks are built from the overlap of words normatively acquired by children prior to 2(1/2) years of age and perceptual and conceptual (functional) features acquired from adult feature generation norms. The resulting networks have small-world structure, indicative of a high degree of feature overlap in local clusters. However, perceptual features--due to their abundance and redundancy--generate networks more robust to feature omissions, while conceptual features are more discriminating and, per feature, offer more categorical information than perceptual features. Using a network specific cluster identification algorithm (the clique percolation method) we also show that shared features among these early-learned nouns create higher-order groupings common to adult taxonomic designations. Again, perceptual and conceptual features play distinct roles among different categories, typically with perceptual features being more inclusive and conceptual features being more exclusive of category memberships. The results offer new and testable hypotheses about the role of shared features in human category knowledge.
authors: Cognitive and Decision Sciences, University of Basel, Basel, Switzerland. thomas.hills@unibas.ch
authors: Thomas T
authors: TT
authors: Hills
authors: Mounir
authors: M
authors: Maouene
authors: Josita
authors: J
authors: Maouene
authors: Adam
authors: A
authors: Sheya
authors: Linda
authors: L
authors: Smith
mesh_terms: D000465:Algorithms; D002675:Child, Preschool; D016000:Cluster Analysis; D006801:Humans; D007804:Language Development; D008959:Models, Neurological; D012660:Semantics; D014825:Vocabulary