THE CROSS-COMPONENT VALIDATION PROJECTION BIAS (CCVPB)

This manuscript proposes the Cross-Component Validation Projection Bias (CCVPB), atheoretical framework concerning the transfer of confidence from one component of a complextask to another component that has not been independently validated to a comparablestandard. The primary case is the Linguistic-Mathematical Validation Projection Case(LMVPC): an individual evaluates a linguistic explanation as correct and nearly flawless,but, without independently auditing the associated advanced mathematical analysis, extendsconfidence in the linguistic component to the mathematical component.The framework distinguishes objective component accuracy, subjective confidence, per-ceived component relatedness, evidential warrant, independent target verification, and thetransfer of confidence. This separation is intended to prevent a valid inference from being mis-classified as a bias merely because information has moved across components. The proposedformal architecture includes a signed projection-weight matrix, an operational definition ofprojection excess relative to a justified benchmark, an N-component directed graph, and aconditional dynamical model of repeated confidence transfer.Twenty interconnected theoretical layers organize the construct, its terminology, formal-ization, measurement, competing explanations, testable predictions, experimental design,statistical analysis, simulation, boundary conditions, and research translation. The manuscriptproposes six initial hypotheses and a staged research programme beginning with the linguisticmathematical primary case.The framework is conceptual rather than empirically established. Its originality, constructvalidity, parameter identifiability, predictive performance, and incremental explanatory valueover established accounts of evaluative spillover and generalization bias remain open researchquestions. The principal contribution claimed here is therefore a structured, falsifiableproposal, not a discovered universal psychological law.Keywords: cross-component confidence transfer; validation; projection bias; evidential warrant;confidence calibration; metacognition; generalization; directed networks; mathematical modeling;falsifiability.