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  1. Cognitive science
  2. Year: 2009, Volume: 33
  3. Year: 2009, Volume: 33, Issue: 4
  4. Conceptual Hierarchies in a Flat Attractor Network
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Year: 2015, Volume: 39
Year: 2014, Volume: 38
Year: 2013, Volume: 37
Year: 2012, Volume: 36
Year: 2011, Volume: 35
Year: 2010, Volume: 34
Year: 2009, Volume: 34
Year: 2009, Volume: 33
Year: 2009, Volume: 33, Issue: 8
Year: 2009, Volume: 33, Issue: 7
Year: 2009, Volume: 33, Issue: 6
Year: 2009, Volume: 33, Issue: 4
On the meaning of words and dinosaur bones: Lexical knowledge without a lexicon
Incrementality and Prediction in Human Sentence Processing
The wind chilled the spectators, but the wine just chilled: Sense, structure, and sentence comprehension
Conceptual Hierarchies in a Flat Attractor Network
Year: 2009, Volume: 33, Issue: 3
Year: 2009, Volume: 33, Issue: 2
Year: 2009, Volume: 33, Issue: 1
Year: 2008, Volume: 32
Year: 2004, Volume: 28
Year: 2002, Volume: 26
Year: 2001, Volume: 25
Year: 1998, Volume: 22

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Conceptual Hierarchies in a Flat Attractor Network

Content Provider PubMed Central
Author O’connor, Christopher M. Cree, George S. Mcrae, Ken
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.
Starting Page 665
File Format PDF
ISSN 15516709
e-ISSN 15516709
Journal Cognitive science
Issue Number 4
Volume Number 33
Language English
Publisher Date 2009-01-01
Access Restriction Open
Subject Keyword Research in Higher Education
Content Type Text
Resource Type Article
Subject Experimental and Cognitive Psychology Artificial Intelligence Cognitive Neuroscience
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