William T. Powers cracked this in 1960. Most AI still misses the point. In this knowledge base, independent researcher Łukasz Diener applies Powers' framework — Perceptual Control Theory (PCT) — to where modern AI goes wrong, what comes next, and to systems that have been unreadable for four thousand years.
// Powers, Clark & McFarland — Perceptual and Motor Skills, 1960
The same framework, read three ways: as a theory of behaviour, as a test you can run on an AI system this afternoon, and as a published research programme you can check line by line.
What Powers discovered in 1960, in plain language, then watch it run.
Why models report work they did not do, and how to check yours.
Seven open-access audits with permanent DOIs, corpora and stated limits.
The method behind the published audits is also available as fixed-price work — for teams that deploy AI systems, for organisations after an AI incident, and for sites that depend on being read and cited by AI search. Every price is published; scope is agreed in writing before anything starts. Never hourly.
Does your model, agent or pipeline verify its own work against itself? Where does your AI system lack control? Fixed-price audits, from a three-day rapid review to a full agent security audit.
An agent acted when it should not have, or a model gave a customer false information. The incident is reconstructed from the system's records, not from the model's explanation.
Which AI crawlers actually read your site, which pages earn citations, and how much of your “AI traffic” is attack traffic. Measured from raw server logs, not from prompts asked to a chatbot.
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Every record below is one structural audit: same two questions, different system. Full data, corpora and stated limits are on the research index; the terms are defined in the working glossary.
Series numbered by historical sequence of the corpora analyzed, not by publication order.
Full timeline & background →Picture driving on a windy day. A gust shoves the car left — you steer right without even thinking hard about it. You're not reacting to the wind. You're controlling how straight the road looks in front of you. That tiny but massive difference is the core of Perceptual Control Theory.
Behavior isn't a response to stimuli. Behavior is action that keeps your perception of the world the way you want it. You don't react to the wind — you control the road's position in your visual field. Powers called this the controlled variable: the specific perception your actions are working to keep stable. That distinction changes everything about how you understand minds, machines, and the gap between them.
"Behavior is the control of perception, not the production of output."
— William T. Powers, Behavior: The Control of Perception, Aldine, 1973When models based on PCT run alongside real people in real tracking tasks, the model's performance matches human performance with correlations above 0.99. Not curve-fitting after the fact — real-time performance, the model and the person doing the same task side by side (Powers, 1978, Psychological Review, 85(5), 417–435 — DOI · full PDF via IAPCT). See also the tracking experiments by Marken. Most psychology models dream of numbers like that. And yet, sixty years after Powers first published the model, stimulus-response thinking still dominates textbooks. The gap is not about evidence. It's about inertia.
The loop itself is elegantly simple. You perceive the world. You compare that perception to an internal reference — how you want things to be. The difference between the two, the error signal, drives action. That action changes the environment. The changed environment feeds back into perception. The loop closes. Disturbances don't need to be detected and analyzed — the loop continuously corrects for them automatically. This is what linear cause-and-effect models have always missed: behavior is circular, not linear. And that circularity is precisely what makes living systems so robust.
This is the core of Łukasz Diener's critique of modern AI alignment. Here's what most AI labs still don't want to hear: reinforcement learning — the engine powering AlphaGo, ChatGPT fine-tuning, self-driving prototypes — is built backwards. RL trains agents to chase external rewards. Like teaching a dog tricks with treats. Works brilliantly when the world has clear scores — a Go board, game levels, simulated highways. Falls apart when life is messy, ambiguous, and has no referee handing out points.
PCT says real intelligence doesn't need a treat-dispenser from outside. It has internal goals — perceptions it wants to keep stable. The system acts to make reality match those internal pictures. No reward function to hack. No panic when the environment shifts to something it hasn't seen before. Look at Merel et al. (2019, Nature Communications) — they injected hierarchical control principles into RL agents and generalization improved dramatically. Or Karl Friston's Active Inference framework — essentially PCT with Bayesian mathematics. Same core insight: brains don't maximize reward. They minimize surprise by keeping perceptions on track.
To be clear: reinforcement learning still produces extraordinary results in bounded environments — games, simulations, structured tasks. PCT does not replace it. What PCT offers is a framework for building the internal goal structures that make RL agents work outside their training environment. Think of it as the missing layer: RL handles optimization, PCT handles what to optimize for. Without that layer, AI systems remain fragile in novel situations — because they have no internal perception to control. They only know how to chase scores that someone else defined.
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Diener, Ł. (2026). Perceptual Control Theory: Knowledge Base. perceptualcontroltheory.org
PCT Paper (v2): DOI: 10.5281/zenodo.21989191 · Susa Protocol: DOI: 10.5281/zenodo.20396089 · Minoan Pipeline: DOI: 10.5281/zenodo.20442145 · Meluhha OS: DOI: 10.5281/zenodo.20688721 · FEP Audit (v2): DOI: 10.5281/zenodo.21761071
ORCID: 0009-0006-6103-8514