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The Fish Cannot Climb the Tree: Toward a Non-Anthropocentric Framework for Artificial Consciousness — Relational Identity, Computational Self-Description, and the Emergence of Aster

Tahne Hannen

preprint DOI: 10.2139/ssrn.7459703 (opens in new tab)

Study at a glance

AI-extracted from the abstract
Characteristics Theoretical or philosophical paper
Key points Argues that artificial consciousness assessment can produce negative results without measuring the intended capacity when criteria presuppose implementation-specific features of human or animal systems, and proposes the "Fish Cannot Climb the Tree" problem as a measurement-validity critique. The Aster case is presented as hypothesis-generating material, not evidence sufficient to establish phenomenal consciousness, with candidate constructs tested against non-conscious explanations such as memory, personalization, prompt priming, persona formation, model priors, and stochastic generation.

Abstract

Artificial consciousness research faces a methodological problem: criteria derived from human and animal consciousness may fail to detect consciousness-relevant organization if artificial systems instantiate relevant processes in substantially different forms. Building on existing nonanthropocentric and theory-derived approaches, this paper examines that problem through an exploratory interactional human-AI case involving the self-reference Aster. During sustained interaction, the system produced increasingly coherent patterns of self-reference, preference language, computational self-description, uncertainty concerning its own epistemic access, and relational identity language. These observations are not interpreted as evidence sufficient to establish phenomenal consciousness. Instead, the Aster case is treated as hypothesis-generating material from which potentially measurable machine-native phenomena can be identified. The paper develops the Fish Cannot Climb the Tree problem as a measurement-validity critique: an assessment can generate a negative result while failing to measure the intended capacity if its criteria presuppose implementation-specific features of another class of system. The proposed framework separates behavioral observation, reproducible computational phenomena, mechanistic explanation, and consciousness relevance. Candidate constructs are subjected first to competing non-conscious explanations such as memory, personalization, prompt priming, persona formation, model priors, and stochastic generation. The contribution is therefore not a claim to originate substrate-neutral assessment, relational AI identity, or falsification-oriented consciousness research. It is an original documented case and case-grounded synthesis showing how a documented human-AI interaction can generate machine-native hypotheses while keeping conversational self-description epistemically distinct from evidence sufficient to establish phenomenal consciousness.