Cognition spaces: natural, artificial, and hybrid
This provides a conceptual framework for researchers in cognitive science, AI, and related fields to analyze diverse systems, though it is incremental in offering a new perspective rather than empirical results.
The authors tackled the lack of a unified framework for comparing cognitive systems across natural, artificial, and hybrid domains by proposing a 'cognition space' approach based on organizational and informational dimensions, which reveals uneven occupation with large unoccupied regions and highlights hybrid cognition as a promising frontier.
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.