Date of Award
Spring 5-2017
Degree Type
Thesis
Degree Name
M.S.
Degree Program
Computer Science
Department
Computer Science
Major Professor
Ware, Stephen G.
Second Advisor
Summa, Christopher
Third Advisor
Hoque, Tamjidul
Abstract
Indexter is a plan-based model of narrative that incorporates cognitive scientific theories about the salience—or prominence in memory—of narrative events. A pair of Indexter events can share up to five indices with one another: protagonist, time, space, causality, and intentionality. The pairwise event salience hypothesis states that when a past event shares one or more of these indices with the most recently narrated event, that past event is more salient, or easier to recall, than an event which shares none of them. In this study we demonstrate that we can predict user choices based on the salience of past events. Specifically, we investigate the hypothesis that when users are given a choice between two events in an interactive narrative, they are more likely to choose the one which makes the previous events in the story more salient according to this theory.
Recommended Citation
Farrell, Rachelyn, "Predicting User Choices in Interactive Narratives using Indexter's Pairwise Event Salience Hypothesis" (2017). University of New Orleans Theses and Dissertations. 2319.
https://scholarworks.uno.edu/td/2319
Rights
The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.