We examined the influence of the pre-existing organiza-tion of the semantic memory on forming new episodic associations between words. Testing human subjects' performance we found that a semantic relationship be-tween words facilitates forming episodic associations be-tween them. Furthermore, the amount of facilitation in-creases linearly as a function of the number of co-occurrence of the words, up to a ceiling. Constrained by these empirical findings we developed a computational model, based on the theory of spreading activation over semantic networks. The model uses self-organizing maps to represent semantic relatedness, and lateral connections to represent the episodic associations. When two words are presented to the model, the interaction of the two ac-tivation waves is summed and added to the direct lateral connection between them. The main result is that the model is capable of replicating the empirical results. The model also makes several testable predictions: First, it should be easier to form an association from a word with few semantic neighbors to a word with many se-mantic neighbors than vice-versa. Second, after associat-ing an unrelated word pair it should be easier to associate another two words each related to one of the words in the first pair. Third, a less focused activation wave, which may be the cause of schizophrenic thought disor-der, should decrease the advantage in learning rate of re-lated over unrelated pairs