A Computational Implementation of the CCV Framework: Preliminary Evidence for Emergent Psychological Dynamics from Meaning Architecture

Current approaches to modeling psychological phenomena typically program specific behaviors directly, resulting in systems that simulate rather than generate mental dynamics. This paper presents a computational implementation of the Curiosity-Clarity-Valence (CCV) framework, a theoretical architecture proposing that meaningful conscious experience arises from the interaction of pre-linguistic interrogative demands, valence-weighted crystallization, contradiction-driven entropy accumulation, and conditional curiosity gating. Using a large language model (LLM) as a perception layer for semantic extraction, we implemented the CCV architecture and tested its ability to produce documented psychological phenomena without explicit programming. Preliminary results demonstrate that the architecture generates directionally correct patterns for: (1) differential trauma responses between secure and insecure developmental histories, (2) learned helplessness patterns where opportunities are rejected by minds crystallized around futility beliefs, and (3) systematic preference for demonstrated actions over verbal reassurance in wounded psychological configurations. These preliminary findings emerged from structural dynamics rather than programmed rules, suggesting that the CCV architecture may capture something fundamental about how meaning forms and transforms. This proof-of-concept implementation requires further testing and parameter refinement, but provides initial evidence for the framework's validity. Implications for computational psychiatry, AI alignment, and the development of artificial systems with genuine understanding are discussed. Keywords: meaning architecture, computational psychology, consciousness, crystallization, interrogative structure, emergent dynamics, proof of concept