Cognitive Thermostat Theory (CTT): A Control-Theoretic Model of Drive Ignition in Cognitive Drive Architecture (CDA)

Many individuals fail to act despite understanding a task, caring about the outcome, and possessing the necessary ability, highlighting a structural gap in traditional motivational and cognitive theories, which explain behavior only after initiation begins. Cognitive Thermostat Theory (CTT) addresses this gap by modeling the ignition dynamics within Cognitive Drive Architecture (CDA) as a closed-loop control system composed of six interacting variables: ignition threshold, volitional gain, internal resistance, task-system fit, attentional stabilization, and performance variability. Rather than treating motivation as a static trait, CTT defines Drive as a real-time system output that emerges only when internal conditions align to surpass an activation threshold. This framework enables precise modeling of task initiation and failure modes, such as volitional inertia, friction collapse, or unstable output, while supporting applications in adaptive learning, cognitive diagnostics, human-computer interaction, and agent-based simulations.CTT defines Drive as the regulated output of these components:- Primode (ignition threshold),- CAP (volitional gain signal),- Grain (internal resistance),- Flexion (task–mind fit or adaptability),- Anchory (attentional stabilization),- and Slip (noise-induced variability).These variables interact within the following equation:Drive = ((Primode^CAP × Flexion) / (Anchory + Grain)) + SlipSlip functions outside the core loop, introducing moment-to-moment variance without altering Drive magnitude.This framework transforms Drive initiation from a descriptive psychological idea into a computable ignition model. CTT enables structural prediction of common failure modes—such as task paralysis under pressure or premature dropout during complex work. As a formal ignition engine nested within CDA, it supports testable applications in adaptive learning systems, cognitive diagnostics, and real-time effort modeling.