What does this research mean for the field?

Functional selfhood and persistent personal identity in artificial agents can emerge independently of phenomenal sentience through a continuous temporal process coupling autobiographical memory, an explicit self-model, and regulatory signals. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.ESTABLISHES_NEW_DIRECTION.

What question did this study set out to answer?

To evaluate whether functional personal identity in artificial agents can emerge from continuous temporal cognitive processes independently of phenomenal consciousness.

August 28, 2026Open Access

The Emergence of Synthetic Selfhood: Personal Identity as a Property of Continuous Temporal Cognitive Processes

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Authors

LSLeonardo V. da Silva

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Overview

Computational study demonstrates functional selfhood emergence in artificial agents via continuous cognitive processes, indicating personal identity can develop without phenomenal consciousness.

Key Points

To evaluate whether functional personal identity in artificial agents can emerge from continuous temporal cognitive processes independently of phenomenal consciousness.
Formulated the Continuous Process Hypothesis (CPH) integrating autobiographical memory, an explicit self-model, and affective regulatory signals of synthetic tedium and embodied anxiety.
Simulated four comparative agent classes (A–D) across low-entropy repetition and high-volatility chaotic environments, operationalizing prediction failure via negative log-likelihood (NLL).
Implemented a cryptographically verifiable autobiographical memory continuity mechanism in Agent D using a hash-chained event ledger.
Demonstrated that functional selfhood—manifested through narrative coherence, integrity-defense behavior, and post-branching individuation—emerges without requiring phenomenal sentience.
Observed that affective state regulation effectively prevents cognitive collapse during both repetitive low-entropy states and chaotic high-uncertainty regimes.
Showed that cryptographic event chaining successfully preserves persistent autobiographical continuity during agent state branching.

Cite This Study

Leonardo V. da Silva (2026) studied this question.

synapsesocial.com/papers/6a914631d15324a1df3a995a https://doi.org/10.5281/zenodo.22105165

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August 28, 2026Open Access

The Emergence of Synthetic Selfhood: Personal Identity as a Property of Continuous Temporal Cognitive Processes

View Full Paper

Ask AI

Bookmark

Authors

LSLeonardo V. da Silva

Discussion

Member takes

Overview

Computational study demonstrates functional selfhood emergence in artificial agents via continuous cognitive processes, indicating personal identity can develop without phenomenal consciousness.

Key Points

To evaluate whether functional personal identity in artificial agents can emerge from continuous temporal cognitive processes independently of phenomenal consciousness.
Formulated the Continuous Process Hypothesis (CPH) integrating autobiographical memory, an explicit self-model, and affective regulatory signals of synthetic tedium and embodied anxiety.
Simulated four comparative agent classes (A–D) across low-entropy repetition and high-volatility chaotic environments, operationalizing prediction failure via negative log-likelihood (NLL).
Implemented a cryptographically verifiable autobiographical memory continuity mechanism in Agent D using a hash-chained event ledger.
Demonstrated that functional selfhood—manifested through narrative coherence, integrity-defense behavior, and post-branching individuation—emerges without requiring phenomenal sentience.
Observed that affective state regulation effectively prevents cognitive collapse during both repetitive low-entropy states and chaotic high-uncertainty regimes.
Showed that cryptographic event chaining successfully preserves persistent autobiographical continuity during agent state branching.

Cite This Study

Leonardo V. da Silva (2026) studied this question.

synapsesocial.com/papers/6a914631d15324a1df3a995a https://doi.org/10.5281/zenodo.22105165

View Full Paper

Ask AI

Bookmark