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Semantic Boost on Episodic Associations: An Empirically‐Based Computational Model
Author(s) -
Silberman Yaron,
Bentin Shlomo,
Miikkulainen Risto
Publication year - 2007
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 - 10.1080/15326900701399921
Subject(s) - episodic memory , semantics (computer science) , semantic memory , associative learning , associative property , cognitive psychology , word (group theory) , natural language processing , computer science , psychology , semantic network , computational model , artificial intelligence , cognition , linguistics , neuroscience , mathematics , philosophy , pure mathematics , programming language
Words become associated following repeated co‐occurrence episodes. This process might be further determined by the semantic characteristics of the words. The present study focused on how semantic and episodic factors interact in incidental formation of word associations. First, we found that human participants associate semantically related words more easily than unrelated words; this advantage increased linearly with repeated co‐occurrence. Second, we developed a computational model, SEMANT, suggesting a possible mechanism for this semantic‐episodic interaction. In SEMANT, episodic associations are implemented through lateral connections between nodes in a pre‐existent self‐organized map of word semantics. These connections are strengthened at each instance of concomitant activation, proportionally with the amount of the overlapping activity waves of activated nodes. In computer simulations SEMANT replicated the dynamics of associative learning in humans and led to testable predictions concerning normal associative learning as well as impaired learning in a diffuse semantic system like that characteristic of schizophrenia.