概念学习在一个概率的思维语言. 它是如何可能的,它前提是什么?
1Tilburg Center for Logic and Philosophy of Science (TiLPS), Tilburg University, Tilburg, The Netherlands m.colombo@uvt.nl; https://mteocolphi.wordpress.com/.
The Behavioral and brain sciences
|September 28, 2023
概括
本研究探讨了概率思维语言 (PLoT) 和概念学习的起源. PLoT架构可能支持概念学习,但它们对非PLoT模型的优势尚不清楚.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 思想的哲学 思想的哲学
背景情况:
- 在认知中的象征性表示的性质是有争议的.
- 思想语言 (LoT) 假设提出了内部符号系统.
- 概率方法提供了一个在不确定性下推理的框架.
研究的目的:
- 探索概率思维语言 (PLoT) 的起源.
- 在PLOT.中通过概率推理来研究概念学习.
- 解决LoT的循环性异议,并评估PLoT的解释能力.
主要方法:
- 对LoT循环性异议的理论探索.
- 对概率推理机制的分析.
- 基于PLoT的认知架构与替代方案的比较.
主要成果:
- 基于PLoT的认知架构表明了真正的概念学习的潜力.
- 该研究概述了克服对LoT.传统循环性异议的方法.
- 目前的证据并没有明确地确定与非LoT架构相比,在概念学习中对PLoT的解释范围更大.
结论:
- 概率思维语言为支持概念学习的认知架构提供了一个可行的框架.
- 需要进行进一步的研究,以澄清PLoT在解释概念获取方面比其他模型具有的具体优势.
- 解决循环性异议对于在认知科学中推进基于LoT的理论至关重要.
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