Gespeichert in:
| 1. Verfasser: | |
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| Format: | Recurso digital |
| Sprache: | Englisch |
| Veröffentlicht: |
Zenodo
2026
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| Schlagworte: | |
| Online-Zugang: | https://doi.org/10.5281/zenodo.18570794 |
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Inhaltsangabe:
- <p>“Far transfer is rare” is among the most robust findings in educational psychology. But rare for<br>whom? This paper identifies an unstated boundary condition in the learning transfer literature:<br>virtually all canonical transfer studies draw participants exclusively from neurotypical populations.<br>Cognitive architecture is neither reported as a variable nor controlled as a moderator. We identify<br>this as Boundary Condition Dropout (BCD)—the systematic loss of validity conditions when<br>findings are generalized beyond their original scope—and argue that it has produced a theoretical<br>distortion that constrains the field’s central constructs. Evidence from four literatures converges on a<br>single conclusion: learning transfer is a property of cognitive architectures, not of domain pairs.<br>Autistic individuals achieve equivalent abstract reasoning through fundamentally different<br>processing routes. Gifted learners spontaneously abstract structural features without explicit<br>scaffolding. Twice-exceptional learners provide within-person natural experiments showing transfer<br>asymmetries that track cognitive subsystem integrity rather than domain distance. Even within<br>neurotypical populations, every major predictor of transfer success—fluid intelligence, working<br>memory, metacognition, cognitive flexibility—is an architectural variable. The near/far distinction,<br>the field’s central organizational construct, is cognition-dependent: “distance” between domains is<br>computed by the cognitive architecture encountering them, and different architectures compute<br>different distances. We propose that the near/far distinction be parameterized by architecture, that<br>causal claims in transfer research specify their architectural preconditions, and that cognitive profile<br>be treated as an independent variable. Five testable predictions are derived that diverge from the<br>current consensus. The “transfer problem” may be partly an artifact of studying one cognitive<br>architecture and generalizing to all.</p>