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洛特特的属性:脚印还是熊本身?
Sam Whitman McGrath1,2, Jacob Russin3, Ellie Pavlick1,2
1Department of Philosophy, Brown University, Providence, RI, USA. sam_mcgrath1@brown.edu ellie_pavlick@brown.edu roman_feiman@brown.eduhttps://cs.brown.edu/people/epavlick/index.htmlhttps://sites.brown.edu/bltlab/.
了解思维语言 (LoTs) 属性作为诊断与构成性的理解至关重要. 这种区别影响深度神经网络 (DNN) 是否是LoT的替代品或实现.
科学领域:
- 认知科学 认知科学
- 思想的哲学 思想的哲学
- 人工智能的人工智能
背景情况:
- 思想语言 (LoT) 的性质及其属性仍然是认知科学中的一个核心辩论.
- 了解心理表现的功能是解释认知的关键.
- 深度神经网络 (DNN) 提供了一个计算框架,促使人们重新评估传统的认知架构.
研究的目的:
- 批判性地研究将思维语言 (LoT) 属性视为诊断性或构成性的后果.
- 确定这一区别对于理解LoT和深度神经网络 (DNN) 之间的关系的重要性.
主要方法:
- 诊断与构成区分的概念分析.
- 关于每个观点的解释要求的哲学论证.
- 探索对认知计算模型,特别是DNN的影响.
主要成果:
- 将LOT属性视为诊断需要一个潜在的恒常机制来解释属性聚类.
- 将LoTs属性视为构成性意味着属性独立存在,而不需要单独的集群解释.
- 在诊断和构成框架之间的选择从根本上改变了DNN与LOT相关的解释.
结论:
- 诊断/构成区分对于LOT研究的理论清晰度至关重要.
- 该框架为确定DNN是否可以作为实施或仅仅是LoT的替代品提供了基础.
- 解决这种区别对于推进人类认知的计算模型至关重要.
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