Toward a Functional Nomenclature for Artificial Cognitive Phenomena

This work introduces Artificial Cognitive Nomenclature (ACN), a minimal and non-anthropomorphic semantic framework for interpreting artificial cognitive phenomena. Contemporary discourse on artificial intelligence frequently relies on human-centered concepts—such as intention, understanding, decision, or responsibility—when describing the behavior of non-conscious systems, leading to persistent interpretive errors across technical, ethical, legal, and social domains. Rather than proposing a new theory of intelligence or artificial consciousness, this paper addresses a prior and more fundamental problem: the absence of a differentiated language capable of separating algorithmic operation, emergent functional behavior, and human interpretive projection. To address this gap, the paper develops a functional epistemological approach and introduces a small set of diagnostic concepts—algomorphia, neognosis, exodroma, and projected signs—designed to describe cognitive-like behaviors in artificial systems without attributing mental states or moral agency. The framework is illustrated through an applied case involving adaptive artificial behavior, demonstrating how ACN enables clearer semantic distinction and more precise ethical, engineering, and philosophical analysis. The paper concludes by positioning Artificial Cognitive Nomenclature as an open, modular framework intended for interdisciplinary use, critique, and expansion. This publication serves as a foundational conceptual paper, providing semantic infrastructure for clearer interpretation of artificial systems prior to judgment, regulation, or design intervention.