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. (shrink)

This paper systematizes the word search potential of DreamBank.net by formulating and testing a set of word strings that can be used as default analytic categories in future investigations. The word strings are applied to the 981 dream reports of college students gathered by Hall and Van de Castle and the 136 dream reports of an 80-year old male gathered by Bulkeley . The results show a basic compatibility with the frequencies identified by Hall and Van de Castle’s labor-intensive method (...) of content analysis employing teams of human coders. These findings support the expanded use of word search technologies for the scientific study of dream content and its relation to forms of waking consciousness. (shrink)

This paper shows how the dream archive and search engine on DreamBank.net, a Web site containing over 22,000 dream reports, can be used to generate new findings on dream content, some of which raise interesting questions about the relationship between dreaming and various forms of waking thought. It begins with studies that draw dream reports from DreamBank.net for studies of social networks in dreams, and then demonstrates the usefulness of the search engine by employing word strings relating to religious and (...) sexual elements. Examples from two lengthy individual dream series are used to show how the dreams of one person can be studied for characters, activities, and emotions. A final example shows that accurate inferences about a person’s religious beliefs can be made on the basis of reading through dreams retrieved with a few keywords. The overall findings are similar to those in studies using traditional forms of content analysis. (shrink)