Toward Enactive Artificial Intelligence

2026-05-22

AI Summary

This study addresses a fundamental limitation in mainstream artificial intelligence, which typically treats cognition as internal computation decoupled from the body and environment, thereby overlooking the inseparability of perception and action, embodiment, autonomy, and the dynamic, interactive nature of experience. For the first time, this work systematically introduces the four core principles of enactive cognition—sensorimotor coupling, experience-dependence, embodiment, and intrinsic normativity—into AI research, aligning them with reinforcement learning frameworks to reveal both structural resonances and theoretical gaps. The paper not only evaluates how closely existing AI systems approximate these enactive principles but also proposes concrete pathways for their integration, laying a theoretical foundation for developing next-generation agents that are embodied, interactive, and genuinely agentic, thereby advancing AI toward forms of intelligence more closely aligned with biological cognition.

Abstract

In this paper, we advocate for incorporating enactive approaches to perception and cognition into artificial intelligence (AI). Enactive approaches view perception as an active, skillful engagement with the world, where agents perceive by acting and by understanding how their actions shape their experience. This contrasts with classical views that treat perception as a passive internal process in which the brain receives sensory input, processes it, and issues commands for action. Enactive views emphasize the dynamic, embodied, and interactive character of perception, grounded in the lived experience of agents embedded in their environments. We identify and develop four key enactive concepts that we find most relevant to AI: experience, action perception inseparability, autonomy, and embodiment. Much of mainstream AI, from classical rule based systems to large language models, has largely neglected these insights, treating cognition as internal processing detached from embodied interaction and intrinsic normativity. Reinforcement learning (RL), however, exhibits structural resonance with enactive principles through its emphasis on action, agent environment interaction, feedback driven adaptation, and agent centered evaluation. However, this resonance should not be taken as theoretical equivalence, as RL approximates some enactive insights, but key elements remain absent or weakly developed. Building on this analysis, we suggest a broader incorporation of enactive ideas into both mainstream AI and RL.

Problem

• enactive cognition
• embodied intelligence
• perception-action coupling
• autonomy
• artificial intelligence

Innovation

• enactive AI
• embodiment
• action-perception cycle
• autonomy
• reinforcement learning