A Computational Implementation of the Event-Indexing Situation Model for the Algorithmic Manipulation of Salience

Rogelio Cardona-Rivera, North Carolina State University

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, I co-developed 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. This computational model of narrative proposes 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 the model. A story’s internal structure, however, plays a role in how a reader understands it. Thus, the computational model that we present extends an existing planning-based approach to narrative, which models coherent story structure. We augment this plan-based approach with information that allows us to model the updates being made to a reader’s mental model of the story during online comprehension, that is, during the process of experiencing a narrative. This poster presents the knowledge representation and algorithms that a system will use for the computational generation of narratives that target the activation and manipulation of memory salience.

Abstract Author(s): Rogelio E. Cardona-Rivera