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Conceptual hierarchies in a flat attractor network: dynamics of learning and computations
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In: http://www.utsc.utoronto.ca/~gcree/OConnor_etal_(2009)_Conceptual_hierarchies_[Cog_Sci].pdf (2009)
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Meaning of chipmunk, cherry, chisel, cheese, and cello (and many other such concrete nouns
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In: http://www.utsc.utoronto.ca/~gcree/Cree_McRae_(2003)_Analyzing_the_factors_underlying_[JEPG].pdf (2003)
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An attractor model of lexical conceptual processing: Simulating semantic priming
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In: http://csjarchive.cogsci.rpi.edu/1999v23/i03/p0371p0414/main.pdf (1999)
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An Attractor Model of Lexical Conceptual Processing: Simulating Semantic Priming
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In: http://publish.uwo.ca/~gcree/Papers/creeMcraeMcnorgan99.pdf (1999)
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Everyone’s a Critic: Memory Models and Uses for an Artificial Turing Judge 1
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In: http://agi-conf.org/2009/papers/paper_19.pdf
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Conceptual Hierarchies Arise from the Dynamics of Learning and Processing: Insights from a Flat Attractor Network
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In: http://csjarchive.cogsci.rpi.edu/Proceedings/2006/docs/p2577.pdf
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Abstract:
The structure of people's conceptual knowledge of concrete nouns has traditionally been viewed as hierarchical (Collins & Quillian, 1969). This has occurred primarily because human performance in many tasks (e.g., typicality rating and superordinate to exemplar priming) appear to implicate hierarchies. Thus hierarchies have been transported, rather transparently, into theories of the structure of knowledge. The goal of the present research is to show that behavior that, on the surface, appears to demand a hierarchical model can be simulated using a feature-based attractor network that contains a single level of semantics. In addition, this model provides insight into the role of similarity on the temporal dynamics of conceptual processing. Network Architecture and Training The network mapped from 30 wordform nodes (representing
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URL: http://csjarchive.cogsci.rpi.edu/Proceedings/2006/docs/p2577.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.541.2875
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