AI不仅能模仿人类思维，还能逐步成为人类认知的一部分。
Gradual Cognitive Externalization: From Modeling Cognition to Constituting It
- 提出渐进式认知外化框架，解释AI如何从模拟认知变为构成认知
- 实证发现已有部署系统满足外化前提条件，具备双向适应等特征
- 适用于研究人机融合、意识架构或长寿命数字人格的学者
开发者正发布能复现同事沟通风格、编码导师指导策略，甚至在生物死亡后仍保留个体行为模式的AI代理。为解释这一现象，我们提出渐进式认知外化（GCE）框架：环境中的AI系统通过与用户的持续因果耦合，从建模认知功能转向构成用户认知架构的一部分。该框架采用明确的功能主义立场——认知功能由其因果-功能角色定义，而非物质载体。核心基于行为流形假说和一个可验证的假设：对任何行为输出位于可学习流形上的认知功能，其运作无需不可观测的隐藏成分（无行为不可见残余，NBIR）。我们记录了已部署系统的证据，表明外化的前提条件已可观测；形式化了三个区分认知整合与工具使用的标准（双向适应、功能等价、因果耦合）；并推导出五个理论约束阈值下的可检验预测。
原文摘要 · Abstract (English)
Developers are publishing AI agent skills that replicate a colleague's communication style, encode a supervisor's mentoring heuristics, or preserve a person's behavioral repertoire beyond biological death. To explain why, we propose Gradual Cognitive Externalization (GCE), a framework arguing that ambient AI systems, through sustained causal coupling with users, transition from modeling cognitive functions to constituting part of users' cognitive architectures. GCE adopts an explicit functionalist commitment: cognitive functions are individuated by their causal-functional roles, not by substrate. The framework rests on the behavioral manifold hypothesis and a central falsifiable assumption, the no behaviorally invisible residual (NBIR) hypothesis: for any cognitive function whose behavioral output lies on a learnable manifold, no behaviorally invisible component is necessary for that function's operation. We document evidence from deployed AI systems showing that externalization preconditions are already observable, formalize three criteria separating cognitive integration from tool use (bidirectional adaptation, functional equivalence, causal coupling), and derive five testable predictions with theory-constrained thresholds.
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