Towards a Process of Simulating Narrative Comprehension

Abstract

Simulating narrative comprehension has received little attention despite potentially aiding greatly in narrative generation or aiding in designing narratives. The work must be tied to theories of mind and understanding, and be based on algorithms that are described by psychology itself. For my thesis work, I propose building a cognition-like system based on the unified narrative understanding model: Multiversionality. In order to test the algorithmic model, I present the methods by which I plan to verify the solution: 1. by testing whether updates to prior knowledge that contradict prior assumptions actually affect, slow down, comprehension, 2. by testing how knowledgeable a player is in a given genre speeds up or slows down understanding by varying the input models, and 3. by testing how different refinement strategies could encapsulate human understanding.