-д хадгалсан:
| Үндсэн зохиолч: | |
|---|---|
| Формат: | Recurso digital |
| Хэл сонгох: | англи |
| Хэвлэсэн: |
Zenodo
2026
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| Нөхцлүүд: | |
| Онлайн хандалт: | https://doi.org/10.5281/zenodo.18948016 |
| Шошгууд: |
Шошго нэмэх
Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!
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Агуулга:
- <p>"This paper proposes a formal computational framework in which self-continuity is modeled as probabilistic inference over a latent identity state. By integrating a state-space generative architecture with content-addressable memory retrieval, the model describes identity as a hidden dynamical variable reconstructed through Bayesian updating. The framework introduces an identity potential function that defines a landscape of attractor configurations, allowing for a quantitative analysis of identity stability, gradual drift, and abrupt phase transitions. Five testable empirical predictions are generated concerning memory consistency, precursor shifts, and neural correlates of identity velocity." </p>