Predictive–Structural Judgment Architecture (PSJA) —— 一种整体-并行-预测驱动型阅读与判断认知架构模型

Abstract

This paper proposes the Predictive–Structural Judgment Architecture (PSJA), a transferable cognitive model derived from systematic introspective analysis of holistic-parallel reading behavior.

Unlike conventional serial-compositional cognition, PSJA operates through predictive processing, structural primacy, anomaly sensitivity, and real-time judgment formation. The model integrates insights from cognitive psychology, neuroscience (predictive coding and network interaction), behavioral science, and human–AI collaborative dynamics. It further examines cognitive divergence within genetically identical individuals, the symbolic interface hypothesis for multilingual and programming fluency, and the alignment between cognitive structure and value orientation.

PSJA is proposed as a formalizable architecture applicable to strategic reasoning, governance design, legal risk analysis, AI collaboration, and judgment-centric systems.

本文提出 “预测-结构-判断认知架构模型”(Predictive–Structural Judgment Architecture, PSJA), 基于对 整体-并行-预测驱动型阅读方式的系统化自我认知审计与跨学科整合而构建。

与常见的线性-串行-语义装配型认知不同, PSJA以预测驱动、结构优先、异常敏感与实时判断生成作为核心特征。模型结合认知心理学、神经科学 (预测编码与网络协同)、行为科学以及人机协作机制进行理论整合, 并进一步分析同卵双生个体的认知分化机制、符号接口假设与多语言及编程潜能之间的关系, 以及能力结构与价值取向的一致性问题。

PSJA被形式化为一种可迁移的判断架构, 适用于战略设计、治理建模、法律风险控制、AI协作系统与判断科学研究领域。