揭示自然与人工认知系统中反推理行为的成因与影响
Counter-Inferential Behavior in Natural and Artificial Cognitive Systems
- 通过反馈机制与元认知评估的交互，揭示反推理行为的内在机理
- 发现即使适应良好系统也会因奖励失衡等产生认知僵化
- 适用于研究认知韧性、智能系统设计及人类心理防御机制
本研究探讨自然与人工认知系统中反推理行为的涌现，即个体错误归因经验成功或抑制适应，导致认知僵化或非适应性稳定。分析了典型场景：奖励失衡强化稳定性、将成功归因于内在优越性、在感知模型脆弱时进行保护性重构。此类行为并非源于噪声或设计缺陷，而是内部信息模型、实证反馈与高阶评估机制之间结构化互动的结果。结合人工系统、生物认知、人类心理学与社会动态的证据，识别出反推理行为是一种普遍的认知脆弱性，可在看似适应良好的系统中显现。研究强调在稳定条件下保持最低限度的适应激活的重要性，并提出可抵抗信息压力下僵化的认知架构设计原则。
原文摘要 · Abstract (English)
This study explores the emergence of counter-inferential behavior in natural and artificial cognitive systems, that is, patterns in which agents misattribute empirical success or suppress adaptation, leading to epistemic rigidity or maladaptive stability. We analyze archetypal scenarios in which such behavior arises: reinforcement of stability through reward imbalance, meta-cognitive attribution of success to internal superiority, and protective reframing under perceived model fragility. Rather than arising from noise or flawed design, these behaviors emerge through structured interactions between internal information models, empirical feedback, and higher-order evaluation mechanisms. Drawing on evidence from artificial systems, biological cognition, human psychology, and social dynamics, we identify counter-inferential behavior as a general cognitive vulnerability that can manifest even in otherwise well-adapted systems. The findings highlight the importance of preserving minimal adaptive activation under stable conditions and suggest design principles for cognitive architectures that can resist rigidity under informational stress.
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