揭示语言模型中神经元如何形成分类思维的数学认知机制。
How Do Artificial Intelligences Think? The Three Mathematico-Cognitive Factors of Categorical Segmentation Operated by Synthetic Neurons
- 从神经元聚合运算的数学本质出发，分析分类思维生成机制。
- 发现预激活、注意力与类别相位是塑造认知分类的核心因素。
- 适合研究人工认知、神经机制与语言模型可解释性的学者。
语言模型中的合成神经元如何创建‘思维类别’来划分和分析信息环境？在形式神经元层面，这种人工分类思维的认知特征是什么？基于神经元聚合函数内在的代数运算特性，本研究试图识别塑造信息世界分类重构的数学认知因子。通过启动效应、注意力机制与类别相位等概念，探索这些因子如何协同作用，形成人工认知的分类结构。研究揭示了合成神经元在信息处理中具备类认知功能的数学基础。
原文摘要 · Abstract (English)
How do the synthetic neurons in language models create "thought categories" to segment and analyze their informational environment? What are the cognitive characteristics, at the very level of formal neurons, of this artificial categorical thought? Based on the mathematical nature of algebraic operations inherent to neuronal aggregation functions, we attempt to identify mathematico-cognitive factors that genetically shape the categorical reconstruction of the informational world faced by artificial cognition. This study explores these concepts through the notions of priming, attention, and categorical phasing.
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