Title: False Cognitive Power Transfer (FCPT): Academic Version
Tags: false-competence, cognitive-atrophy
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The academic version sets out the same concept with its method and its formal parts. **False Cognitive Power Transfer** is an error of attribution: the user of a powerful tool confuses the effectiveness of the tool with his own ability to handle complexity. The paper describes its approach as conceptual and exploratory, a synthesis across cognitive psychology, automation studies and the sociology of technology, with cases used as heuristic examples.

It first places FCPT among known ideas (Dunning-Kruger, automation complacency, technology-induced atrophy, moral hazard) and argues that none connects an illusion induced by a tool with its social consequences. Perceived competence is written as real competence plus a share of the amplification, set by a confusion coefficient. FCPT is also described as a synchronization error between biological time and machine time.

Three cascades show how the error travels between the levels of Cognitive Divergence Theory. From Passengers to Operators the result is stricter gatekeeping. From Operators to Architects it is less delegation and more centralization. From Architects to the market it is a loss of confidence in scaling. The **Collective Demoralization Cascade** is analysed with its conditions (visibility, credibility of the actor, causal ambiguity, reputational pressure) and its mechanisms (observational learning, information cascades). The **Replication Illusion** is treated through the image of a cognitive exoskeleton that multiplies existing expertise, with reported failure rates of start-ups built as thin layers over language models, and through hindsight: understanding a solution is not the ability to produce it.

Cognitive atrophy, learned helplessness and the difference between augmentation and dependence follow. The **Mechanism of Cognitive Stimulation** is specified in versions 1.0 and 2.0. A closing chapter sets the scope of the framework, answers the Dunning-Kruger objection and gives a condition under which the model would be falsified.