Algorithmic Stress and the Collapse of Cognitive Load: Methodological Foundations for Informational Periodization in Human-AI Symbiosis
DOI:
https://doi.org/10.56147/jbhs.3.4.158Keywords:
- Human-AI symbiosis,
- Cognitive load theory,
- Informational periodization,
- Algorithmic stress,
- SIOJ
Abstract
This working paper introduces the framework of Informational Periodization Human-AI as a methodological model to mitigate cognitive overload and false fluency in generative artificial intelligence environments. The advancement of language models has generated a paradox: The illusion of efficiency conceals a functional degradation in the quality of interaction, driven by poorly formulated and fragmented commands. By functionally transposing the Principle of Adaptation from sports physiology and integrating it with John Sweller’s Cognitive Load Theory, this manuscript delimits the causes of algorithmic stress. It introduces the concept of Contextual Reconstruction Load and the necessity of an Audit-Ready External Memory to protect human metacognition. Finally, it demonstrates the practical application of this architecture in data governance and the protection of the chain of custody in complex legal environments.
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References
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