Semantic Boost on Episodic Associations: An Empirically Based Computational Model (2007)
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 easier 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.
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Citation:
Cognitive Science, 31:645--671, 2007.
Bibtex:

Shlomo Bentin Collaborator shlomo bentin [at] huji ac il
Risto Miikkulainen Faculty risto [at] cs utexas edu
Yaron Silberman Ph.D. Alumni yarons [at] alice nc huji ac il
DISLEX

This package contains the C-code and data for training and testing the DISLEX model of the lexicon, which is also par...

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