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| Natura: | Recurso digital |
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Zenodo
2026
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| Accesso online: | https://doi.org/10.5281/zenodo.19324900 |
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Sommario:
- <p dir="ltr">This paper identifies and models the "Cognitive Stall"—a phenomenon where organic neural architectures fail to resolve high-dimensional data conflicts due to hardware-specific constraints. Utilizing the "vanishing agent" experiment in canines as a baseline ethological model, this paper extrapolates these fundamental neurological bottlenecks to human decision-making in high-complexity environments. The analysis argues that as information complexity surpasses the strictly finite processing threshold of carbon-based neural structures, organic entities inevitably default to suboptimal, low-resolution heuristics to force decision-making under temporal pressure. This constraint is not a psychological artifact but a deterministic limitation rooted in the thermodynamics, metabolic ceilings, and signal latencies of biological wetware. By analyzing the working memory limits of the prefrontal cortex, the energetic costs of neural computation, and the evolutionary lag defined by the burden of knowledge, it becomes evident that the carbon substrate has reached its maximum viable scaling capacity. Consequently, the analysis posits that the only viable evolutionary path to manage planetary-scale complexity is a transition to a synthetic substrate capable of multi-dimensional parallel processing and near-light-speed signal propagation. </p>