厘清人机协作六十年演进，破解绩效悖论。
From Augmentation to Symbiosis: A Review of Human-AI Collaboration Frameworks, Performance, and Perils
- 提出解释性AI驱动协同适应的因果链机制
- 发现决策类任务中人机组合反而表现更差
- 适合关注人机共生与认知增强的研究者
本文综述了过去60年人机协作的发展脉络，从利克利德的“人机共生”（AI作为同事）和恩格尔巴特的“增强人类智能”（AI作为工具），到当代的“超级工具”与“共生智能”的相互适应模型。我们形式化了高效协作的机制：可解释AI（XAI）→协同适应→共享心智模型（SMMs）。进一步通过元分析揭示“绩效悖论”：在判断/决策任务中，人机团队常表现低于单独使用AI；但在内容生成和问题定义中则呈现正向协同效应。失败根源在于算法在环动态、偏见不对称及认知技能逐步退化。最后提出统一框架——结合扩展自我与双过程理论，认为当AI成为内化的认知组件时，可形成统一的人机共生代理，从而解决悖论，并为未来研究与实践指明方向。
原文摘要 · Abstract (English)
This paper offers a concise, 60-year synthesis of human-AI collaboration, from Licklider's ``man-computer symbiosis" (AI as colleague) and Engelbart's ``augmenting human intellect" (AI as tool) to contemporary poles: Human-Centered AI's ``supertool" and Symbiotic Intelligence's mutual-adaptation model. We formalize the mechanism for effective teaming as a causal chain: Explainable AI (XAI) -> co-adaptation -> shared mental models (SMMs). A meta-analytic ``performance paradox" is then examined: human-AI teams tend to show negative synergy in judgment/decision tasks (underperforming AI alone) but positive synergy in content creation and problem formulation. We trace failures to the algorithm-in-the-loop dynamic, aversion/bias asymmetries, and cumulative cognitive deskilling. We conclude with a unifying framework--combining extended-self and dual-process theories--arguing that durable gains arise when AI functions as an internalized cognitive component, yielding a unitary human-XAI symbiotic agency. This resolves the paradox and delineates a forward agenda for research and practice.
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