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A Neuronal Basis for the Fan Effect
Author(s) -
Goetz Philip,
Walters Deborah
Publication year - 2000
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1207/s15516709cog2401_5
Subject(s) - abstraction , computer science , content addressable memory , associative property , artificial intelligence , basis (linear algebra) , speech recognition , natural language processing , artificial neural network , mathematics , philosophy , epistemology , pure mathematics , geometry
The fan effect says that “activation” spreading from a concept is divided among the concepts it spreads to. Because this activation is not a physical entity, but an abstraction of unknown lower‐level processes, the spreading activation model has predictive but not explanatory power. We provide one explanation of the fan effect by showing that distributed neuronal memory networks (specifically, Hopfield networks) reproduce four qualitative aspects of the fan effect: faster recognition of sentences containing lower‐fan words, faster recognition of sentences when more cues are provided, faster acceptance of studied sentences than rejection of probes, and faster recognition of sentences studied more frequently. These are all a natural result of the dynamics of distributed associative memory.