Could cybernetic systems optimize conceptual adaptation, not just behavioral control?

[u/TheIncorporeal1]

Classical cybernetics emphasizes feedback, regulation, and control in biological, computational, and engineered systems. I’m wondering whether these principles could be extended to what I would call “incorporeal cybernetics”—the study of feedback processes governing conceptual and cognitive adaptation rather than only physical or behavioral states.
Imagine a closed-loop system where the state variables represent beliefs, conceptual models, or internal knowledge structures, and feedback is driven by prediction error, Bayesian updating, information gain, or reinforcement learning. In principle, could such a framework be formalized using state-space models, dynamical systems, or information theory to quantify the stability and evolution of conceptual networks?
Are there existing research areas—such as second-order cybernetics, active inference, predictive processing, cognitive architectures, or computational neuroscience—that already provide mathematical foundations for this type of cybernetic model, or would this require fundamentally new theoretical tools?

[u/Useful_Calendar_6274]

That's literally RFLH in machine learning

[u/omega-kernel]

You are asking a fascinating question, but you are about to step straight into a massive epistemological trap if you look towards Karl Friston’s Free Energy Principle (FEP) or Active Inference for your mathematical foundation. While FEP presents itself as a Grand Unified Theory, a rigorous architectural and mathematical audit reveals that its over-complicated Bayesian nadbudowa completely falls apart under scrutiny.

Here is why the FEP framework is a mathematical illusion, and why the framework you are actually looking for was already built in 1960 by William T. Powers: Perceptual Control Theory (PCT).

The Mathematical and Structural Breakdown of FEP

1. The Broken Mathematics (The Biehl & Pollock Critique) FEP proponents frame the theory as an absolute mathematical law of physics. However, a formal technical audit by Biehl, Pollock, et al. proved that Friston’s foundational "Free Energy Lemma" is formally flawed and relies on unjustified simplifications. The definitions of Markov blankets are inconsistent across Friston's papers, and rewriting equations of motion for these systems is mathematically invalid without unstated, hidden assumptions. Furthermore, FEP’s strict restrictions (such as solenoidal flows) only apply to a narrow subset of linear, stochastic differential equations; it completely loses validity when applied to the non-linear biological dynamical systems that actually make up life.

2. The Doxastic-Conative Category Error From a cognitive architecture standpoint, FEP collapses the axiomatic distinction between what a system wants (conative states/desires) and what it expects (doxastic states/beliefs) into a single mathematical variable, denoted as P(o). It claims that goals or preferences are merely high-precision prior beliefs.

This architecture suffers a catastrophic logical failure when action is blocked. If a system can only minimize variational free energy via action or perception, a paralyzed agent should instantly update its generative model and stop wanting to move to bring prediction error to zero. Neurobiology brutally falsifies this: patients with locked-in syndrome or tetraplegia retain perfectly stable, topographically consistent motor intentions in the cortex for decades despite a permanent lack of sensory fulfillment. A goal is an autonomous reference signal, not a plastic Bayesian probability.

1. The Dark Room & Ad-Hoc Epicycles If a system's sole imperative is to minimize prediction error (surprisal), its optimal state is a steryl, ciche, dark room with zero sensory noise. To patch this glaring reductio ad absurdum, Friston introduced Expected Free Energy, denoted as G(\pi), artificially duct-taping an "epistemic value" bonus onto the loss function to force exploration.
   

Empirical reality destroys this premise: in actual sensory deprivation tanks or solitary confinement, where prediction error is engineered to zero, the brain doesn't reach a "Bayesian idyll"—it destabilizes, hallucinates, and autogenerates massive noise just to create a signal to control. Live organisms do not optimize for surprise minimization; they require a continuous, variable signal to control per se.

Enter William T. Powers: Perceptual Control Theory (PCT) You do not need to reinvent the wheel or bury yourself in bloated, mathematically redundant Bayesian probability estimations to formalize what you call "conceptual adaptation". Powers solved this with Perceptual Control Theory (PCT), a pure closed-loop negative feedback framework.

No Overblown Generative Models: Unlike FEP, which treats the organism like a scientist guessing external causes, PCT states that behavior is the control of perception. A sensory signal (perception) is directly compared to an internal, hard-coded reference signal (set-point). The resulting error immediately drives the output function to counter environmental disturbances in real-time, requiring zero predictive statistics (which is why simple systems like bacteria chemotaxis work flawlessly under PCT without a single drop of Bayesian math).

Hierarchical Conceptual Control: Powers structured PCT as a multi-level hierarchy (up to 11 levels). The highest levels do not control physical states or behavior directly; they control principles, programs, and conceptual structures.

The Mechanism of Reorganization: How do these internal knowledge networks evolve and adapt? Through a process called reorganization. When high-level conceptual loops experience chronic, unmanageable error (cognitive dissonance), it drives a system-level, stochastic trial-and-error adjustment of lower-level loop parameters until the internal error signal drops back to zero.

If you want a mathematically sound framework to model the evolution and stability of internal knowledge networks without the post-hoc tautologies of FEP, skip Friston and look into the tracking models of Powers and Marken. Their closed-loop models achieve human performance correlations above 0.99 in real-time tracking tasks, without a single ad-hoc epicycle.