Explainable Structured Bayesian Hypothesis Selection: A Formal Framework for Human-like, Interpretable Belief Formation and Iterative Thought

We propose a formal, testable framework that models human "selection of viewpoints" as a structured, explainable Bayesian hypothesis-selection process. Each candidate viewpoint is treated as a discrete hypothesis; the cognitive state is a joint selection vector over hypotheses whose probability is governed by evidential potentials and structured relational factors (support, conflict, causation). Rather than casting selection as a linear dimensionality reduction (e.g., PCA), we cast it as a structured MAP/posterior-mass selection problem over combinatorial hypothesis subsets subject to interpretability and parsimony constraints. We present (1) a generative factor-graph formalization, (2) objective functions combining posterior mass, model complexity, and explainability penalties, (3) tractable approximate inference and greedy MAP algorithms suitable for cognitive modeling or AI reasoning modules, and (4) an iterative Candidate–Select–Update (CSU) algorithm that captures the loop of hypothesis generation, adjudication, adoption, and subsequent proposal of novel hypotheses. We outline experimental paradigms (behavioral and computational) to validate the model and discuss applications to explainable AI, chain-of-thought reasoning in LLMs, and algorithmic implementations.