Cognitive processes are realized across an extraordinary range of natural, artificial, and hybrid systems, yet there is no unified framework for comparing their forms, limits, and unrealized possibilities. Here, we propose a cognition space approach that replaces narrow, substrate-dependent definitions with a comparative representation based on organizational and informational dimensions. Within this framework, cognition is treated as a graded capacity to sense, process, and act upon information, allowing systems as diverse as cells, brains, artificial agents, and human-AI collectives to be analyzed within a common conceptual landscape. We introduce and examine three cognition spaces -- basal aneural, neural, and human-AI hybrid -- and show that their occupation is highly uneven, with clusters of realized systems separated by large unoccupied regions. We argue that these voids are not accidental but reflect evolutionary contingencies, physical constraints, and design limitations. By focusing on the structure of cognition spaces rather than on categorical definitions, this approach clarifies the diversity of existing cognitive systems and highlights hybrid cognition as a promising frontier for exploring novel forms of complexity beyond those produced by biological evolution.

翻译：认知过程在自然系统、人工系统以及混合系统构成的广阔谱系中得以实现，然而目前缺乏一个统一的框架来比较它们的形式、局限以及尚未实现的可能形态。在此，我们提出一种认知空间方法，该方法摒弃了狭隘的、依赖于基质的定义，转而采用基于组织维度和信息维度的比较性表征。在此框架内，认知被视为一种感知、处理并基于信息采取行动的分级能力，这使得从细胞、大脑、智能体到人机协同体等各不相同的系统，都能在一个共同的概念图景中得到分析。我们引入并考察了三种认知空间——基础无神经型、神经型以及人机混合型——并表明这些空间的占据情况极不均衡，已实现的系统聚集成簇，其间被大片未占据区域所分隔。我们认为这些空白并非偶然，而是反映了演化偶然性、物理约束以及设计局限。通过聚焦于认知空间的结构而非分类定义，本方法澄清了现有认知系统的多样性，并强调混合认知是一个充满前景的前沿领域，可用于探索超越生物演化所产生的新型复杂性形式。