This paper introduces the concept of ``generative midtended cognition'', exploring the integration of generative AI with human cognition. The term "generative" reflects AI's ability to iteratively produce structured outputs, while "midtended" captures the potential hybrid (human-AI) nature of the process. It stands between traditional conceptions of intended creation, understood directed from within, and extended processes that bring exo-biological processes into the creative process. We examine current generative technologies (based on multimodal transformer architectures typical of large language models like ChatGPT), to explain how they can transform human cognitive agency beyond what standard theories of extended cognition can capture. We suggest that the type of cognitive activity typical of the coupling between a human and generative technologies is closer (but not equivalent) to social cognition than to classical extended cognitive paradigms. Yet, it deserves a specific treatment. We provide an explicit definition of generative midtended cognition in which we treat interventions by AI systems as constitutive of the agent's intentional creative processes. Furthermore, we distinguish two dimensions of generative hybrid creativity: 1. Width: captures the sensitivity of the context of the generative process (from the single letter to the whole historical and surrounding data), 2. Depth: captures the granularity of iteration loops involved in the process. Generative midtended cognition stands in the middle depth between conversational forms of cognition in which complete utterances or creative units are exchanged, and micro-cognitive (e.g. neural) subpersonal processes. Finally, the paper discusses the potential risks and benefits of widespread generative AI adoption, including the challenges of authenticity, generative power asymmetry, and creative boost or atrophy.

翻译：本文提出“生成式中间延伸认知”概念，探讨生成式人工智能与人类认知的融合。术语“生成式”反映人工智能迭代产生结构化输出的能力，“中间延伸”则捕捉该过程潜在的人机混合特质——它居于传统意图性创造（被视为内向引导方向）与延伸进程（引入外生物过程至创造性过程）之间。我们通过考察当前基于多模态Transformer架构（典型如ChatGPT等大语言模型）的生成技术，阐释其如何超越经典延伸认知理论的阐释边界，重塑人类认知主体性。研究表明，人类与生成技术耦合的典型认知活动更接近（但不等于）社会认知而非经典延伸认知范式，因此需要专门的理论框架。我们给出生成式中间延伸认知的明确定义，将AI系统的干预视作主体意向性创造过程的构成部分。此外，我们区分生成式混合创造力的两个维度：1. 广度：捕捉生成过程对上下文（从单个字母到整体历史与周边数据）的敏感性；2. 深度：捕捉过程中迭代循环的粒度层级。生成式中间延伸认知居于会话式认知（交换完整话语或创造单元）与微认知（如神经层面的亚个体过程）的中间深度。最后，本文探讨生成式AI广泛应用的潜在风险与收益，包括真实性挑战、生成权力不对称、创意提升或萎缩等问题。