利用Indexter的成对事件显著性假设影响交互叙述中的用户选择

Rachelyn Farrell, Stephen G. Ware
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引用次数: 3

摘要

Indexter是一个基于计划的叙事模型,它结合了关于叙事事件的显著性(或记忆中的突出性)的认知科学理论。一对Indexter事件可以彼此共享多达五个索引:主角、时间、空间、因果关系和意向性。两两事件显著性假设认为,如果过去的事件与最近叙述的事件具有一个或多个这些指标,那么过去的事件就更加显著。在之前的研究中,我们使用该模型基于先前事件的指数来预测用户在交互式故事中的选择。我们现在表明,我们可以用同样的方法来影响他们做出某些选择。在这项研究中,参与者阅读了一个有两种可能结局的互动故事。我们通过操纵故事事件的显著性来影响他们选择一个特定的结局。我们发现用户非常喜欢目标结局。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Influencing User Choices in Interactive Narratives Using Indexter's Pairwise Event Salience Hypothesis
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 a past event is more salient if it shares one or more of these indices with the most recently narrated event. In a previous study we used this model to predict users’ choices in an interactive story based on the indices of prior events. We now show that we can use the same method to influence them to make certain choices. In this study, participants read an interactive story with two possible endings. We influenced them to choose a particular ending by manipulating the salience of story events. We showed that users significantly favored the targeted ending.
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