The Cognitive Intelligence Framework for Scientific Discovery: A Reflexive Model of Understanding Stability (CRF) and Self-Consistent AI Cognition
Year: 2025
Type: article
Abstract: The Cognitive Reflexivity Function (CRF) introduces a new paradigm for understanding epistemic stability within intelligent systems. It measures the divergence between perception and reality — the volatility of understanding itself — and integrates behavioral, informational, and reflexive dimensions into a unified cognitive metric. This paper applies the CRF framework to both financial and artificial cognition contexts, demonstrating how reflexive architectures like ALEX can achieve self-consistent reasoning through continuous minimization of interpretive error. Historical validation across major speculative cycles (1637–2025) shows how CRF captures collective cognitive distortions before systemic crises. By linking cognitive coherence to measurable data, CRF provides a foundation for developing reflexive, transparent, and self-correcting artificial intelligences.
Author: Minchev, Teodor Boyanov
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7103174777
FWCI:
Cites:
Cited by:
Related to: