认知架构中的内在动机:智力好奇心起源于模式发现
Kazuma Nagashima1, Junya Morita1,2, Yugo Takeuchi1
1Department of Information Science and Technology, Graduate School of Science and Technology, Shizuoka University, Hamamatsu, Japan.
Frontiers in artificial intelligence
|November 1, 2024
概括
由模式发现驱动的智力好奇心,根据认知处理水平不同,会对任务性能产生不同的影响. 这项研究使用ACT-R架构模拟好奇心,提供了对内在动机的见解.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 心理学 心理学 心理学
背景情况:
- 强化学习模型将好奇心作为任务执行的内在动机.
- 以前的研究缺乏对好奇心驱动的表现背后的认知机制的深入研究.
- 现有的模型不能完全捕捉到人类对内在动机的认知观点.
研究的目的:
- 提出和建模一个用于智力好奇心的认知机制,专注于模式发现.
- 研究智力好奇心如何通过认知功能影响任务参与和完成.
- 为了比较一种新的认知架构模型的好奇心与传统的强化学习方法.
主要方法:
- 开发基于思维理性 (ACT-R) 架构的自适应控制的计算模型.
- 实现了"模式匹配"",实用性"和"生产编译"功能来表示好奇心.
- 模拟了三个具有不同认知水平的ACT-R模型,在操纵的好奇心强度下在迷宫中导航.
- 将模型性能与强化学习内在好奇心模块 (ICM) 的性能进行比较.
主要成果:
- 智力好奇心对较低层次思维模型中的任务完成产生了负面影响.
- 智力好奇心在更高层次的思维模型中对任务完成产生了积极的影响.
- 与ICM相比,拟议的认知机制在表示代理意图方面表现出优势.
结论:
- 基于ACT-R的模型为理解智力好奇心和内在动机提供了认知框架.
- 模式发现是智力好奇心的一个关键因素,影响任务参与度.
- 这项研究强调了认知架构在模拟创新的内在动机方面的重要性.
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