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Spreading Activation in an Attractor Network With Latching Dynamics: Automatic Semantic Priming Revisited
Author(s) -
Lerner Itamar,
Bentin Shlomo,
Shriki Oren
Publication year - 2012
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 - 10.1111/cogs.12007
Subject(s) - computer science , attractor , priming (agriculture) , associative property , semantic memory , artificial intelligence , semantic network , content addressable memory , stimulus (psychology) , network dynamics , mechanism (biology) , cognitive psychology , cognitive science , artificial neural network , natural language processing , neuroscience , psychology , cognition , mathematics , physics , discrete mathematics , mathematical analysis , botany , germination , biology , quantum mechanics , pure mathematics
Localist models of spreading activation (SA) and models assuming distributed representations offer very different takes on semantic priming, a widely investigated paradigm in word recognition and semantic memory research. In this study, we implemented SA in an attractor neural network model with distributed representations and created a unified framework for the two approaches. Our models assume a synaptic depression mechanism leading to autonomous transitions between encoded memory patterns (latching dynamics), which account for the major characteristics of automatic semantic priming in humans. Using computer simulations, we demonstrated how findings that challenged attractor‐based networks in the past, such as mediated and asymmetric priming, are a natural consequence of our present model’s dynamics. Puzzling results regarding backward priming were also given a straightforward explanation. In addition, the current model addresses some of the differences between semantic and associative relatedness and explains how these differences interact with stimulus onset asynchrony in priming experiments.