Neural Networks as Embodied Observers of Complexity: An Enactive Approach

This article explores a conceptual framework for understanding neural networks through the lens of the enactivist paradigm, a philosophical theory that posits that cognition arises from the dynamic interaction of an organism with its environment. We explore how neural networks, as complex adaptive systems, transcend their traditional role as computational machines and become active participants in their data-rich environment, evolving through continuous feedback and adaptation. Drawing parallels with biological systems, we argue that artificial neural networks exhibit what enactivists call “structural coupling” – symbiotic co-evolution with their information ecosystems. From this perspective, knowledge is not passively processed but actively constructed through repetitive interactions, each of which shapes the internal state of the system in a self-organizing manner similar to the sensorimotor activity of natural organisms. This approach goes beyond classical computational theories by emphasizing that machine cognition resembles human-like cognitive processes, an emergent form of “world creation.” Our analysis shows that these artificial entities have focal points, or internal observers, associated with patterns learned during training, suggesting that neural networks shape worldviews through active participation rather than passive observation. The paper reconceptualizes machine learning models as cognitive agents that bring new forms to our understanding of cognition and signals an epistemological shift in which knowledge itself is seen as participation and creation mediated by technologically complex but organically similar structures. This has important implications for both technical applications and theoretical debates in cognitive science, potentially changing the way we think about what cognition means in artificial and natural intelligence.