Interoceptive machine framework: Toward interoception-inspired regulatory architectures in artificial intelligence
This review proposes an integrative framework grounded on interoception and embodied AI-termed the interoceptive machine framework-that translates biologically inspired principles of internal-state regulation into computational architectures for adaptive autonomy. Interoception, conceived as the monitoring, integration, and regulation of internal signals, has proven relevant for understanding adaptive behavior in biological systems. The proposed framework organizes interoceptive contributions into three functional principles: homeostatic, allostatic, and enactive, each associated with distinct computational roles: internal viability regulation, anticipatory uncertainty-based re-evaluation...
| Authors | Diego Candia-Rivera |
|---|---|
| Primary category | cs.AI |
| Published | 27 April 2026 |
| Last updated at source | 27 April 2026 |
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