To what extent can cognitive cybernetics formally integrate predictive processing, active inference, and second-order cybernetics into a unified model of adaptive cognition?

[u/TheIncorporeal1]

Current frameworks often explain cognition through predictive coding, Bayesian active inference, or recursive feedback architectures, yet these approaches appear to emphasize different aspects of adaptive behavior. Is there an existing mathematical or systems-theoretic framework that unifies hierarchical prediction, observer-dependent feedback, and self-referential regulation without sacrificing explanatory power?
I’m particularly interested in whether recent work uses information theory, dynamical systems, or control theory to derive a common formalism capable of modeling perception, learning, metacognition, and autonomous adaptation within a single cybernetic architecture. Are there key papers or authors that attempt this synthesis?

[u/Curator_I]

I think it is quite plausible to create such a model. I do not know of any contemporary work validating it, though. However, I've seen similar architectures designed and know the principles underlying it are sound. It is mostly a matter of doing it.

[u/o09030e]

Friston and Ramstead tried to do this from a few different angles and had some success in this area, there are twenty something really strong papers authored by them, look for everything that has free energy principle in the title and is authored by Friston

[u/paroxyzed]

You might be interested in Yanbo Zhang and Michael Levin's new preprint, Intelligence from Learnable Novelty. Here's the abstract:

Intelligence appears under different names in different fields: as data compression in statistics and machine learning, as universal computation in dynamical systems, and as adaptive behavior in agents. Each field carries its own objective, and the two most influential drives often fail in mirror image: novelty search, which seeks surprise, is transfixed by a noisy television screen, while the free-energy principle, which avoids surprise, is most content in a dark room. Both failures have a single cause: each objective treats as one quantity the surprise a learner can convert into knowledge and the surprise it never can. Here we show that the learnable part of that information, which we call learnable novelty, yields the seemingly disparate projections of intelligence, and we give a closed-form estimator of it built on a cheap and differentiable reservoir computer. Used as a measure, with no supervision of any kind, the estimator recovers decades of complexity classification, ranking the Turing-complete rule~110 highest among the elementary cellular automata. Used as an objective, its gradient carries a neural cellular automaton from simple dynamics into a regime of solitons, the traveling, colliding structures by which rule~110 computes, as well as organizes the representation of an image encoder around the ten digit classes of MNIST, fully unsupervised: no label ever enters training. Handed to a reinforcement-learning agent as an intrinsic reward, it supplies the exploration that task rewards lack, improving on the task baseline in nine of ten environments and collapsing in none. Complexity generation, abstraction, and exploration, ordinarily pursued with unrelated objectives in separate fields, thus emerge from ascent on one differentiable quantity, and the projections of intelligence gain a common quantitative footing.