The Epistemology of Non-Symbolic Cognition

非符号认知的认识论

• 批准号: 9711202
• 负责人: Robert Cummins
• 金额: $ 7万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-10-01 至 1998-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9711202&HistoricalAwards=false
• 关键词: Epistemology; Non; Symbolic; Cognition

A central problem in understanding the brain concerns how knowledge is stored in synaptic connections among neurons. In artificial neural networks this phenomenon is modeled by connection weights, which determine how much of the activation of one neuron is passed to another neuron. The goal of this project is to understand how knowledge is stored in a set of connection weights, and to use this understanding as the foundation for an epistemology that is directly grounded in what we know of the brain. Our understanding of the brain and of the neural networks that model it, including those used for artificial intelligence purposes, will be limited until we understand how to correlate specific changes in connection weights with specific changes in stored knowledge. For similar reasons, psychological explanations of cognitive processes and the philosophic theories of epistemology that build on them both rely fundamentally on understanding what knowledge is stored, and under what conditions, within a neural network.

理解大脑的一个中心问题涉及知识如何存储在神经元之间的突触连接中。 在人工神经网络中，这种现象是通过连接权重来建模的，连接权重决定了一个神经元的激活量被传递到另一个神经元。 该项目的目标是了解知识如何存储在一组连接权重中，并将这种理解用作直接基于我们对大脑的了解的认识论的基础。 在我们了解如何将连接权重的特定变化与存储知识的特定变化关联起来之前，我们对大脑和对其建模的神经网络（包括用于人工智能目的的神经网络）的理解将受到限制。 出于类似的原因，认知过程的心理学解释和建立在其基础上的认识论哲学理论都从根本上依赖于理解神经网络中存储了什么知识以及在什么条件下存储知识。