A Subsymbolic Model of Complex Story Understanding (2005)
A computational model of story understanding is presented that is able to process stories consisting of multiple scripts. This model is built from subsymbolic neural networks, but unlike previous such models, it can handle stories of variable structure and length. The model can successfully parse and paraphrase script-based stories that share long sequences of common events, with no confusion between the stories. It also exhibits several aspects of human behavior, including robustness to small changes in the sequence of events and emotion priming effects in response to ambiguous cues. It can therefore serve as a foundation for testing theories of normal and impaired story processing in humans.
In Proceedings of the 27th Annual Meeting of the Cognitive Science Society, 2005.

Peggy Fidelman Former Ph.D. Student
Ralph E. Hoffman Collaborator ralph hoffman [at] yale edu
Risto Miikkulainen Faculty risto [at] cs utexas edu