A multi-granularity narrative cloud selection framework for architectural meaning-making integrating the PIA model and three-way decision theory.

Architectural experience depends not only on perceptual fluency but also on whether users can successfully construct higher-order semantic meaning from complex built form. In the Pleasure-Interest Model of Aesthetic Liking (PIA model), the controlled-processing stage differentiates interest, confusion, and boredom. In conventional architectural cognition research, however, confusion is often treated as a negative indicator of communicative failure. This paper challenges that assumption and argues that confusion is especially important in architectural meaning-making because it identifies a population that still possesses interpretive motivation but has not yet achieved semantic resolution due to a mismatch between narrative granularity and cognitive accessibility. To address this problem, the paper proposes a conceptual and heuristic framework that integrates the PIA model, three-way decision theory, and granular computing. First, the three states in the controlled-processing stage of the PIA model are reinterpreted through a three-way decision structure, in which confusion is reconstructed as a strategic boundary region rather than as a purely negative endpoint. Second, a multi-granularity optimization mechanism and a utility-based selection function are introduced to identify narrative descriptions that balance novelty and comprehensibility for target perceivers. Third, a memorial-hall case and a computer-simulated illustrative statement-scoring exercise are used to illustrate the feasibility of the proposed procedure. Rather than claiming full empirical validation, the paper positions its contribution as a design-oriented framework for improving architectural communication, user-centered narrative intervention, and the cultural-social dimension of human-centered sustainability in the built environment.