当新经验带来新知识时:一种计算框架,用于正式化认识的变革性经验
Joan Danielle K Ongchoco1,2, Isaac M Davis2, Julian Jara-Ettinger2,3
1Department of Psychology, The University of British Columbia.
Open mind : discoveries in cognitive science
|November 15, 2024
概括
新的经历可以是变革性的,教导代理人他们是什么样的人. 使用部分可观测的马尔科夫决策过程 (POMDPs) 的计算框架将其与非变革性学习区分开来,为认知科学提供了洞察力.
科学领域:
- 认知科学 认知科学
- 计算神经科学是一种神经科学.
- 思想的哲学 思想的哲学
背景情况:
- 学习可以通过新的经验或通过推断和想象力扩展现有知识来进行.
- 区分真正的新型学习和渐进的知识获取对于理解认知过程至关重要.
研究的目的:
- 提出一个计算框架,以正式区分认知转化和非转化经验.
- 为了确定一个可测量的"签名"的认识转化经验.
- 探索先前知识如何影响新环境中的学习.
主要方法:
- 利用部分可观测的马尔科夫决策过程 (POMDPs) 来建模代理学习.
- 开发一个计算框架,以正式化认识系统的变化.
- 以"平原"为灵感进行合成实验,使用在2D和3D环境之间过渡的代理.
主要成果:
- 知识学变革的经验使得先前的知识变得过时,类似于"从头开始学习".
- 非变革性经验可以通过利用现有知识来促进学习.
- 对于变革性经验,已经确定了一个可测量的签名.
结论:
- 计算框架成功地正式化了变革性和非变革性学习之间的区别.
- 这项工作提供了一种新的方法,用于使用计算工具评估认识论变化的哲学概念.
- 这些发现对了解代理人在新的或扩展的环境中如何适应和学习有意义.
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