AbstractThis paper proposes the Recursive Projection-Appraisal Theory of Deterministic Bias(RPAT-DB), an integrative framework for investigating how people interpret persons, objects,events, fictional entities, and other targets through prior knowledge, beliefs, values, experiences,and evolving mental models. The framework focuses on a proposed phenomenon termedDeterministic Bias (DB): a possible discrepancy between the determinacy a person subjectivelyattributes to an interpretation and the determinacy warranted by the available evidence. Theproposed mechanism begins with an initial interpretive projection, proceeds through theaccumulation and integration of information and backstory, and may produce a coherentcausal narrative that feels more conclusive than its evidential basis justifies. The resultingappraisal may then interact with personal values, social norms, ideological commitments,public labeling, and feedback-driven updating across time.The framework distinguishes the internal interpretive appraisal wall from the externalnormative-labeling wall while treating them as interacting analytical levels rather than inde-pendent systems. Its architecture comprises fifteen analytical layers, a proposed primary caseof Backstory-Induced Deterministic Bias (BIDB), and a candidate mathematical formulationbased on belief distributions, entropy-derived concentration measures, evidence-based referencedistributions, confidence calibration, and recursive state updating. The paper specifies testablehypotheses, measurement requirements, factorial experiments, multilevel statistical models,model-recovery procedures, cross-context validation, alternative explanations, and falsificationconditions.The mathematical expressions are explicitly treated as candidate models rather thanestablished universal laws. The framework's central scientific question is whether the proposedDB construct provides reliable and incremental explanatory or predictive value beyond existingaccounts of confirmation bias, hindsight bias, illusion of explanatory depth, overconfidence, andcoherence-based reasoning. The present contribution is therefore a formalized and falsifiableresearch program, not a report of completed empirical validation.
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Shamiul Hoque Shan (2026) studied this question.
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