Indexter: A Computational Model of the Event-Indexing Situation Model for Characterizing Narratives

Previous approaches to computational models of narrative have successfully considered the internal coherence of the narrative’s structure. However, narratives are also externally focused and authors often design their stories to affect users in specific ways. In order to better characterize the audience in the process of modeling narrative, we introduce Indexter: a computational model of the Event-Indexing Situation Model, a cognitive framework which predicts the salience of previously experienced events in memory based on the current event the audience is experiencing. We approach computational models of narrative from a foundational perspective, and feel that salience is at the core of comprehension. If a particular narrative phenomenon can be expressed in terms of salience in a person’s memory, the phenomenon, in principle, is representable in our model. This paper provides the fundamental bases of our approach as a springboard for future work which will use this model to reason about the audience’s mental state, and to generate narrative fabula and discourse intended to achieve a specific narrative effect.