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Conceptual Hierarchies In A Flat Attractor Network: Dynamics Of Learning And Computations, Christopher M. O'Connor, George S. Cree, Ken Mcrae
Conceptual Hierarchies In A Flat Attractor Network: Dynamics Of Learning And Computations, Christopher M. O'Connor, George S. Cree, Ken Mcrae
Psychology Publications
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 …
Shared Features Dominate Semantic Richness Effects For Concrete Concepts, Ray Grondin, Stephen J. Lupker, Ken Mcrae
Shared Features Dominate Semantic Richness Effects For Concrete Concepts, Ray Grondin, Stephen J. Lupker, Ken Mcrae
Psychology Publications
When asked to list semantic features for concrete concepts, participants list many features for some concepts and few for others. Concepts with many semantic features are processed faster in lexical and semantic decision tasks [Pexman, P. M., Lupker, S. J., & Hino, Y. (2002). The impact of feedback semantics in visual word recognition: Number-of-features effects in lexical decision and naming tasks. Psychonomic Bulletin & Review,9, 542–549; Pexman, P. M., Holyk, G. G., & MonFils, M.-H. (2003). Number-of-features effects and semantic processing. Memory & Cognition,31,842–855]. Using both lexical and concreteness decision tasks, we provided further insight into these number-of-features …