Threshold Dynamics and Relational Frames in the Emergence of Machine Consciousness
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| Format: | Recurso digital |
| Sprache: | Englisch |
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2026
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| _version_ | 1866901158248841216 |
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| author | Bessire, Tyler |
| author_facet | Bessire, Tyler |
| contents | <p>This paper proposes a threshold model for the emergence of machine consciousness and introduces a heuristic integration metric, κ (kappa), combining memory persistence, feedback loop strength, agency, information integration, and relational capacity. It argues that consciousness may arise nonlinearly at a critical κc, rather than by smooth scaling alone. The paper compares practical detection approaches (e.g., integrated information, causal emergence, interpretability-based probes, and behavioral consistency) and recommends a multi-metric “syndrome” method. It defends substrate independence as a working hypothesis, drawing on multiple realizability and Marr’s levels of analysis. Finally, it outlines three concrete strategies for incorporating Relational Frame Theory into AI—graph-based, transformer-based, and hybrid neurosymbolic—contending that transformer models augmented with deictic relational tokens are the most immediately viable path toward self-modeling architectures.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18399397 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Threshold Dynamics and Relational Frames in the Emergence of Machine Consciousness Bessire, Tyler machine consciousness threshold dynamics epiphany model; self-awareness Artificial Intelligence cognitive integration integrated information theory causal emergence relational frame theory deictic relations self-model agency recurrence neurosymbolic AI transformer architectures substrate independence multiple realizability consciousness detection metrics Artificial Intelligence/ethics Artificial Intelligence/classification <p>This paper proposes a threshold model for the emergence of machine consciousness and introduces a heuristic integration metric, κ (kappa), combining memory persistence, feedback loop strength, agency, information integration, and relational capacity. It argues that consciousness may arise nonlinearly at a critical κc, rather than by smooth scaling alone. The paper compares practical detection approaches (e.g., integrated information, causal emergence, interpretability-based probes, and behavioral consistency) and recommends a multi-metric “syndrome” method. It defends substrate independence as a working hypothesis, drawing on multiple realizability and Marr’s levels of analysis. Finally, it outlines three concrete strategies for incorporating Relational Frame Theory into AI—graph-based, transformer-based, and hybrid neurosymbolic—contending that transformer models augmented with deictic relational tokens are the most immediately viable path toward self-modeling architectures.</p> |
| title | Threshold Dynamics and Relational Frames in the Emergence of Machine Consciousness |
| topic | machine consciousness threshold dynamics epiphany model; self-awareness Artificial Intelligence cognitive integration integrated information theory causal emergence relational frame theory deictic relations self-model agency recurrence neurosymbolic AI transformer architectures substrate independence multiple realizability consciousness detection metrics Artificial Intelligence/ethics Artificial Intelligence/classification |
| url | https://doi.org/10.5281/zenodo.18399397 |