Closed-loop cognitive state control: a hypothesis framework for real-time estimation, stabilization, and recovery of task-specific high-performance cognitive states

Human performance often fails not because latent capacity is absent, but because access to high-functioning cognitive states is unstable under fatigue, distraction, overload, and pressure. This hypothesis paper proposes closed-loop cognitive state control-informally, supercognition-as a neuroengineering objective: rapid entry into, stabilization of, and recovery of an individual's highest available task-specific cognitive state. The framework treats cognition as a partially observable dynamical system whose latent states would be inferred from multimodal behavioral, physiological, contextual, and neural signals, and specifies a five-layer architecture comprising multimodal sensing, latent-state inference, predictive transition modeling, closed-loop intervention, and a longitudinal personal neural twin. The proposal is situated against existing closed-loop and neuroadaptive architectures, with an explicit statement of what is and is not new. The state ontology is given as a set of falsifiable assumptions rather than an intuitive taxonomy. Candidate computational implementations are specified for each layer, including hidden semi-Markov and switching state-space models, Bayesian and Kalman filtering, sequence models, hazard models, contextual bandits, model-predictive control, and offline reinforcement learning. The personal neural twin receives an operational specification covering its data requirements, update schedule, calibration and drift-monitoring criteria, and failure modes. Predictive and causal claims are separated, with the evidentiary requirements stated for each. The paper engages directly with the literature that challenges the framework's feasibility, including biomarker reliability, mixed neurofeedback findings, cross-task and cross-subject generalization failure, and the ecological-validity gap in wearable neurotechnology. Operational definitions, application scenarios, a validation protocol with power and diversity requirements, an expanded ethics and governance analysis, and a staged research roadmap are provided. No component of the architecture has been implemented or evaluated here: no simulation, clinical trial, or empirical efficacy claim is presented, and the validation programme described is prospective. The contribution is a falsifiable systems-engineering hypothesis for cognitive-state control.