一个跨学科的观点在语言人类之外的好奇心:动物,婴儿,和人工智能
Sofia Forss1,2, Alejandra Ciria3, Fay Clark4
1Collegium Helveticum, Institute for Advanced Studies, University of Zurich, ETH Zurich and Zurich University of the Arts, Zurich, Switzerland.
Biological reviews of the Cambridge Philosophical Society
|January 29, 2024
概括
好奇心驱动学习和解决问题的能力. 本综述综合了跨学科的研究,提出了从动物到人工智能在各种系统中量化好奇心的建议.
科学领域:
- 认知科学 认知科学
- 比较心理学比较心理学
- 发展心理学 发展心理学
背景情况:
- 好奇心,定义为内在动机的获取信息,对于学习和决策至关重要.
- 研究历来将人类的好奇心与其他动物分开,造成了科学局.
- 这种分离阻碍了对认知连续性的理解和基于好奇心的AI的发展.
研究的目的:
- 综合跨学科的研究对好奇心在非语言系统.
- 为了弥合人类和动物好奇心研究之间的差距.
- 提出一个框架来量化各种各样的代理人的好奇心.
主要方法:
- 在动物行为,发育心理学,神经科学和计算认知方面进行文献综合.
- 对专注于非语言好奇心的研究进行分析.
- 确定共同的发现和方法.
主要成果:
- 关于好奇心的类似发现存在于动物行为,发育心理学和神经科学中.
- 跨学科的整合揭示了信息搜索的共同机制.
- 一个统一的方法来研究好奇心是可行的.
结论:
- 整合对非语言系统的研究促进了对好奇心的理解.
- 量化好奇心使得跨物种,发育阶段和人工剂的操作研究成为可能.
- 这种框架可以为以自然好奇心为灵感的人工智能的发展提供信息.
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