提出可解释的智能系统理论框架，兼具生物合理性与计算高效性。
Model of human cognition
- 基于神经理论构建认知智能的统一框架
- 支持决策与问题解决等认知过程建模
- 适合追求可解释AI的科研与工程人员
大型语言模型的发展受限于可解释性不足、缺乏统一理论以及高昂的运行成本。本文提出一种神经理论框架，用于解释智能系统的涌现，该框架在功能上稳健且具备生物合理性。模型为决策与问题解决等认知过程提供了理论洞察，并提供了一种计算高效的可解释、通用人工智能构建方法。
原文摘要 · Abstract (English)
The development of large language models (LLMs) is limited by a lack of explainability, the absence of a unifying theory, and prohibitive operational costs. We propose a neuro-theoretical framework for the emergence of intelligence in systems that is both functionally robust and biologically plausible. The model provides theoretical insights into cognitive processes such as decision-making and problem solving, and a computationally efficient approach for the creation of explainable and generalizable artificial intelligence.
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