Learning to Use Narrative Function Words for the Organization and Communication of Experience

• Published in: Frontiers in psychology Vol. 12; p. 591703
• Main Authors: Pointeau, Gregoire, Mirliaz, Solène, Mealier, Anne-Laure, Dominey, Peter Ford
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 03.03.2021
• Subjects: discourse marker; narrative; narrative practice; Psychology; reservoir computing; situation model; situation model; reservoir computing; discourse marker; narrative; narrative practice
• ISSN: 1664-1078; 1664-1078
• DOI: 10.3389/fpsyg.2021.591703

How do people learn to talk about the causal and temporal relations between events, and the motivation behind why people do what they do? The narrative practice hypothesis of Hutto and Gallagher holds that children are exposed to narratives that provide training for understanding and expressing reasons for why people behave as they do. In this context, we have recently developed a model of narrative processing where a structured model of the developing situation (the situation model) is built up from experienced events, and enriched by sentences in a narrative that describe event meanings. The main interest is to develop a proof of concept for how narrative can be used to structure, organize and describe experience. Narrative sentences describe events, and they also define temporal and causal relations between events. These relations are specified by a class of narrative function words, including “because, before, after, first, finally.” The current research develops a proof of concept that by observing how people describe social events, a developmental robotic system can begin to acquire early knowledge of how to explain the reasons for events. We collect data from naïve subjects who use narrative function words to describe simple scenes of human-robot interaction, and then employ algorithms for extracting the statistical structure of how narrative function words link events in the situation model. By using these statistical regularities, the robot can thus learn from human experience about how to properly employ in question-answering dialogues with the human, and in generating canonical narratives for new experiences. The behavior of the system is demonstrated over several behavioral interactions, and associated narrative interaction sessions, while a more formal extended evaluation and user study will be the subject of future research. Clearly this is far removed from the power of the full blown narrative practice capability, but it provides a first step in the development of an experimental infrastructure for the study of socially situated narrative practice in human-robot interaction.