Quantity, Organization, and Adaptive Cognitive Reconfiguration: A Dynamical Theory of Relational Coherence

Cognitive processing is often characterized in terms of the amount or complexity of infor-
mation that must be represented and manipulated. However, the same quantity of relational
information can generate different processing requirements when the relations are organized
differently and when the representation is subjected to perturbation. We propose a dy-
namical theory of relational coherence in which cognitive demand is defined as the cost of
maintaining and restoring coherence within an organized relational system. The framework
distinguishes relational quantity Q from organizational structure O and decomposes pro-
cessing demand into maintenance and perturbation-repair components. Repair demand can
propagate through dependency structures, creating a maintenance–repair trade-off in which
organization supports coordinated processing under stable conditions but can increase the
consequences of distributed perturbation. We further model adaptive coherence restoration
as competition among propagation, segmentation, and reconfiguration strategies. A tem-
poral pressure variable integrates repair demand over time, allowing identical cumulative
perturbations to produce different outcomes depending on their temporal distribution. Fi-
nally, a reduced-order nonlinear dynamical model formalizes multistability, fold transitions,
hysteresis, and critical slowing near organizational reconfiguration. Computational demon-
strations show that dependency organization can alter repair requirements at fixed relational
quantity and that the proposed strategy competition generates ordered regime transitions
across a broad parameter region. The framework yields testable predictions concerning
quantity–organization interactions, perturbation propagation, temporal clustering, adaptive
strategy transitions, hysteresis, and recovery dynamics. The theory is proposed as an in-
termediate level account linking relational structure to adaptive cognitive reconfiguration
rather than as a mechanistic neural model