Date of Award


Degree Type


Degree Name

Master of Science




Dr. Ken McRae


The structure of people’s conceptual knowledge of concrete nouns has traditionally been viewed as hierarchical (Collins & Quillian, 1969). Specifically, superordinate concepts (vegetable) are assumed to reside at a higher level than basic-level concepts (carrot). A feature-based attractor network with a single layer of semantic features developed representations of basic-level and superordinate concepts. No hierarchical structure was built into the network. In Experiment and Simulation 1 the graded structure of categories (typicality ratings) are accounted for by the flat attractor-network. Experiment and Simulation 2 show that, as with basic-level concepts, the model predicts feature verification latencies for superordinate concepts (vegetable ). In Experiment and Simulation 3, counterintuitive findings regarding the temporal dynamics of similarity in semantic priming are explained by the model. By treating both types of concepts the same in terms of representation and learning, the model provides new insights to the similarities and differences between them.



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