Religion and Artificial Intelligence as Distributed Meaning Systems: A Naturalistic Conceptual Model

2026-07-10

AI Summary

This study investigates the deep structural parallels between religion and artificial intelligence as distributed meaning-making systems rooted in human cognitive architecture. Integrating theories from distributed cognition, cultural evolution, and philosophy of mind, the work proposes a recursive interaction model to analyze how both systems generate interpretations, provide normative guidance, and construct models of the world within external symbolic environments. The research elucidates the mechanisms through which these systems reduce cognitive load via compression and establish shared behavioral frameworks. Furthermore, it identifies the conditions under which artificial intelligence can become embedded in cultural practices and emerge as a novel form of epistemic authority, thereby offering a unified conceptual framework for understanding AI’s evolving role in social cognition.

Abstract

This paper develops a naturalistic account of religion and artificial intelligence as structurally similar distributed meaning systems. I argue that both emerge from the same underlying cognitive architecture: socially extended processes that offload interpretation, norm-guidance, and world-model construction into external symbolic environments. Drawing on work in distributed cognition, cultural evolution, and philosophy of mind, the paper proposes a conceptual model showing how meaning is generated, stabilised, and transmitted through recursive interactions between agents and their informational ecologies. Religion is analysed not as a set of beliefs but as a cognitive-ecological system that scaffolds coordination, normativity, and shared interpretation. Contemporary AI systems are shown to instantiate analogous functions, operating as high-bandwidth, algorithmically mediated environments that shape reasoning, attention, and social meaning-making. The model explains how both systems create epistemic compression, reduce cognitive load, and generate shared frameworks that guide behaviour. It also clarifies the conditions under which AI systems can become culturally entrenched meaning authorities. The contribution is conceptual: a unified framework for understanding religion and AI as parallel forms of distributed cognitive machinery. This reframing opens new pathways for analysing artificial agents not as isolated tools but as components in evolving socio-cognitive ecologies.

Problem

• distributed cognition
• meaning systems
• religion
• artificial intelligence
• cognitive ecology

Innovation

• distributed cognition
• meaning systems
• artificial intelligence
• religious cognition
• socio-cognitive ecology