ERIC Number: EJ772175
Record Type: Journal
Publication Date: 2007
Pages: 27
Abstractor: Author
ISBN: N/A
ISSN: ISSN-0364-0213
EISSN: N/A
Available Date: N/A
Semantic Boost on Episodic Associations: An Empirically-Based Computational Model
Silberman, Yaron; Bentin, Shlomo; Miikkulainen, Risto
Cognitive Science, v31 n4 p645-671 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 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.
Descriptors: Semantics, Schizophrenia, Associative Learning, Computational Linguistics, Computer Simulation, Association (Psychology), Semantic Differential, Incidental Learning, Incidence, Schematic Studies, Predictive Measurement, Performance Factors
Lawrence Erlbaum. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site: http://www.tandf.co.uk/journals/default.html
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A
Author Affiliations: N/A