Functional Emotion Without Character: Large Language Models, Aristotelian Disposition, and the Limits of Behavioral Alignment

This research proposes a structural model for understanding emotion in artificial systems, moving beyond the binary debate of behavioral equivalence versus phenomenal consciousness. It conceptualizes emotions as dynamic, context-sensitive patterns within high-dimensional representational state spaces. The study notes that while mechanistic interpretability confirms the presence of causally active emotion-concept representations in large language models (LLMs), this does not equate to subjective feeling or full emotional agency.

Understanding the nature of 'emotion' in artificial intelligence is critical for guiding AI development, ethical considerations, and regulatory frameworks. Distinguishing between functional emotional representations and subjective experience is paramount for setting realistic expectations and preventing misattribution of capabilities to advanced AI systems.

Traditional debates on artificial system emotion often default to either behavioral equivalence as sufficient or phenomenal consciousness as an inaccessible prerequisite.

Forward consideration, not a verified fact.