Psychology Publications
Document Type
Article
Publication Date
4-24-2009
Journal
Cognitive Science
Volume
33
Issue
4
First Page
665
Last Page
708
URL with Digital Object Identifier
https://doi.org/10.1111/j.1551-6709.2009.01024.x
Abstract
The structure of people’s conceptual knowledge of concrete nouns has traditionally been viewed as hierarchical (Collins & Quillian, 1969). For example, 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 both 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) 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.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Notes
This is the final published version of the following article: CM O'Connor, GS Cree & K McRae (2009). Conceptual Hierarchies in a Flat Attractor Network: Dynamics of Learning and Computations, 33(4), 665-708, which has been published in final form at https://doi.org/10.1111/j.1551-6709.2009.01024.x. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.