title: Semantic boost on episodic associations: an empirically-based computational model.
abstract: 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.
authors.affiliation: Department of Psychology and Interdisciplinary Center for Neural Computation, The Hebrew University of JerusalemDepartment of Computer Science, The University of Texas at Austin.
authors.forename: Yaron
authors.initials: Y
authors.lastname: Silberman
authors.forename: Shlomo
authors.initials: S
authors.lastname: Bentin
authors.forename: Risto
authors.initials: R
authors.lastname: Miikkulainen