Conceptual Hierarchies in a Flat Attractor Network: Dynamics of Learning and Computations  

Oleh:

O’Connor, Christopher M. ; Cree, George S. ; McRae, Ken

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Cognitive Science vol. 33 no. 4 (Jun. 2009), page 665–708.
Topik: Superordinate concepts; Attractor networks; Temporal dynamics; Semantic memory
Fulltext: 06. Conceptual Hierarchies in a Flat Attractor Network - Dynamics of Learning and Computations.pdf (402.34KB)

Isi artikel

The structure of people’s conceptual knowledge of concrete nouns has traditionally been viewed as hierarchical (Collins & Quillian, 1969). For example, super ordinate 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 both basic-level and super ordinate concepts. No hierarchical structure was built into the network. In Experiment and Simulation 1, the graded structure of categories (typicality ratings) is accounted for by the flat attractor network. Experiment and Simulation 2 show that, as with basic-level concepts, such a network predicts feature verification latencies for superordinate concepts (vegetable ). In Experiment and Simulation 3, counterintuitive results 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, learning, and computations, the model provides new insights into semantic memory.

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