Noosemia: toward a Cognitive and Phenomenological Account of Intentionality Attribution in Human-Generative AI Interaction

TL;DR

This paper introduces Noosemia, a new framework explaining how humans attribute intentionality and agency to generative AI systems through linguistic and phenomenological cues, impacting our understanding of human-AI interaction.

Contribution

It formalizes the concept of Noosemia, linking cognitive and phenomenological aspects of intentionality attribution in human-AI interactions, and distinguishes it from related phenomena.

Findings

Noosemia explains how users perceive agency in AI through linguistic cues.

The framework links LLM meaning construction to human attribution processes.

Introduction of a-noosemia describes phenomenological withdrawal from AI projections.

Abstract

This paper introduces and formalizes Noosem\`ia, a novel cognitive-phenomenological pattern emerging from human interaction with generative AI systems, particularly those enabling dialogic or multimodal exchanges. We propose a multidisciplinary framework to explain how, under certain conditions, users attribute intentionality, agency, and even interiority to these systems - a process grounded not in physical resemblance, but in linguistic performance, epistemic opacity, and emergent technological complexity. By linking an LLM declination of meaning holism to our technical notion of the LLM Contextual Cognitive Field, we clarify how LLMs construct meaning relationally and how coherence and a simulacrum of agency arise at the human-AI interface. The analysis situates noosemia alongside pareidolia, animism, the intentional stance and the uncanny valley, distinguishing its unique characteristics. We also introduce a-noosemia to describe the phenomenological withdrawal of such projections. The paper concludes with reflections on the broader philosophical, epistemological and social implications of noosemic dynamics and directions for future research.