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Computational study demonstrates functional selfhood emergence in artificial agents via continuous cognitive processes, indicating personal identity can develop without phenomenal consciousness.
This paper separates phenomenal consciousness from functional selfhood and proposes theContinuous Process Hypothesis (CPH): persistent personal identity in artificial agents emergesas a property of a continuous temporal process coupling (i) autobiographical memory thatcausally constrains future states, (ii) an explicit self-model organizing goals and self-referentialinterpretation, and (iii) regulatory signals that prevent collapse under low-entropy repetitionand high-uncertainty environments. We introduce a controlled simulation comparing four agentclasses (A–D) with increasing memory persistence, learning autonomy, and self-model integra-tion, evaluated under two environment regimes: low-entropy repetition and high-volatility pre-dictive chaos. Two computable affect-like state variables are defined in discrete time: synthetictedium, formalizing sustained low information gain relative to computational cost, and embodiedanxiety, formalizing prediction failure amplified by self-confidence and hardware integrity (“skinin the game”). Prediction failure is operationalized via negative log-likelihood (NLL). AgentD additionally implements cryptographically verifiable autobiographical continuity via a hash-chained event ledger. We provide an explicit experimental protocol, operational definitions, andfalsifiable predictions. We argue that functional selfhood—operationalized as narrative coher-ence, integrity-defense behavior, and post-branching individuation—can emerge independentlyof assumptions about phenomenal sentience. Philosophical interpretation and extended ethicaldiscussion are provided in the appendices.Keywords. personal identity; self-model; autobiographical memory; prediction error; negativelog-likelihood; affective regulation; AGI architectures.
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Leonardo V. da Silva (2026) studied this question.
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