Cognitive Cybernetics Technical Monograph — Series 1 - 23: Structural Rigidity Without Error

This monograph is the twenty-third in the Cognitive Cybernetics Technical Monograph Series, building on Narrowing of Inference Space (CC-021) and Why New Information Stops Helping (CC-022). It addresses structural rigidity without error—the condition where cognitive systems become rigid while remaining correct, challenging the common error-centric diagnosis of cognitive limitation. The work systematically establishes that cognitive rigidity is commonly diagnosed through errors—incorrect conclusions, logical contradictions, factual inconsistencies—but this diagnostic frame fails when rigidity exists without error. Cognitive systems can be rigid while remaining correct. Structural rigidity is defined as a control configuration in which inference paths are fixed, evaluation weights do not shift, termination criteria dominate, and alternative trajectories are inaccessible. Rigidity is a property of regulation, not accuracy. Errors arise when content conflicts with constraints. In rigid systems, content conforms to stabilized pathways, evaluation filters out disruptive signals, and closure occurs before contradiction emerges, keeping the system internally consistent. Correct outcomes reinforce rigidity by validating existing control parameters, rewarding familiar paths, and discouraging deviation; correctness strengthens constraint rather than loosening it. Rigid systems often appear confident, authoritative, decisive, and consistent—traits often mistaken for mastery but structurally indicating limited navigational freedom. Correction relies on discrepancy detection, evaluation reweighting, and reopening inference paths. In rigid systems, discrepancy thresholds are high, evaluation weights are fixed, and termination overrides revision; correction is processed, not applied. The system does not experience rigidity as limitation; it experiences clarity, efficiency, and certainty—the absence of alternatives is invisible. This pattern is substrate-independent, appearing in human cognition, expert systems, and automated decision engines; the invariant lies in control stabilization. If a system remains correct, resists reframing, repeats stable conclusions, and fails only under novelty, structural rigidity is present. Cognitive systems do not need to be wrong to be rigid. Rigidity exists when control prevents movement, even while correctness persists. Understanding cognitive limitation requires diagnosing rigidity beyond error.