叙述的因果结构和计算价值的叙述
Janice Chen1, Aaron M Bornstein2
1Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA.
Trends in cognitive sciences
|May 11, 2024
概括
由因果关系定义的叙事对于理解人类行为和记忆至关重要. 将叙事因果关系纳入强化学习模型可以提高生态有效性,并澄清故事的作用.
科学领域:
- 认知神经科学 认知神经科学
- 计算神经科学是一种神经科学.
- 心理学 心理学 心理学
背景情况:
- 人类研究经常使用叙事刺激来模仿现实世界的经验.
- 叙述的特点是时间因果关系,这是神经科学中经常被忽视的属性.
- 了解叙事理解和记忆对于认知研究至关重要.
研究的目的:
- 审查关于因果结构如何影响叙事理解和记忆的行为和神经科学研究.
- 探索叙事因果关系与强化学习之间的关系.
- 通过整合叙事原则来增强计算模型的生态有效性.
主要方法:
- 关于叙事处理的行为和神经科学研究的文献评论.
- 在叙事刺激中分析因果结构.
- 概念整合与强化学习框架.
主要成果:
- 因果结构显著影响个人如何理解和回忆叙事.
- 叙述有助于将行动与复杂情景中的结果联系起来.
- 强化学习模型可以从纳入叙事可信度中受益.
结论:
- 因果推理是叙事理解和记忆的基础.
- 叙事结构为建模决策和学习提供了有价值的框架.
- 将叙事因果关系集成到计算模型中,可以提高它们在现实世界中的适用性.
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