Circular Causality: A Short History (with receipts)

“Swans” — M.C. Escher, 1956

TL;DR: Circular causality is the idea that causes and effects can loop back on themselves — and mid-20th century scientists realized these loops aren’t just quirks, but the key to understanding minds, machines, and living systems. Below we trace how pioneers like McCulloch, Wiener, Bateson, von Foerster, Maturana, and Varela flipped the script on “vicious circles,” made feedback into a science, and inspired today’s AI debates about recursive reasoning. Buckle up, it’s loopy history time (with receipts)!

Loops Take the Wheel: Cybernetics in the 1940s–50s

By the 1940s, researchers were getting fed up with strictly linear thinking. Literally — they started focusing on feedback. In 1948 mathematician Norbert Wiener formalized cybernetics as the study of circular causal systems (feedback loops) in animals and machines . Wiener even suggested that all intelligent behavior could be explained by feedback mechanisms — and daringly hinted that machines might simulate these processes. In his view, a thermostat adjusting a heater or a human learning from mistakes both exemplified “cause → effect → cause” loops driving goal-directed behavior.

Around the same time, neuroscientist Warren McCulloch was turning the human brain into a playground of loops. He co-led the famous Macy Conferences on Cybernetics (1946–1953), whose very first title was “Feedback Mechanisms and Circular Causal Systems in Biological and Social Systems” . (Yes, the birth of cybernetics literally had circular causality in the name! Talk about setting the theme.) McCulloch’s big hunch was that neural networks could sustain activity in closed circuits, creating an internal echo we experience as memory or mind. At the 1946 Macy meeting he floated the idea that memory might be a function of “continuous cyclical impulses” in neural nets, i.e. reverberating loops of neuron firing . He described “causal circles” in the brain — feedback loops that could “reverberate indefinitely” and hold information by looping it around. This was radical: rather than a strict input→output chain, the brain could cycle its outputs back into itself, continuously generating its own activity.

Computer science pioneer John von Neumann and others were intrigued too — could a network with loops compute things a step-by-step machine couldn’t? McCulloch and Walter Pitts showed mathematically that neural nets with loops are as powerful as Turing machines, hinting that recursion gives neural brains some of their oomph. It was as if the brain “circuits” its own outputs back into inputs to create thought. No surprise then that Heinz von Foerster, another Macy conferee, later recalled: “Should one name one central concept, a first principle, of cybernetics, it would be circularity.” All the early cybernetics perspectives, he said, arose from this one theme of circular feedback. Once they saw how fertile the idea was, “it was sheer euphoria to philosophize… about its consequences… and unifying power.” 🎉

Indeed, by the mid-20th century, “circularity [had earned] its newfound and rightful respect” among scientists. What had been dismissed as “vicious circles” or logical paradoxes became virtuous loops to be harnessed. Wiener’s anti-aircraft gun predictors, McCulloch’s brain models, Claude Shannon’s information systems — all embraced feedback as a feature, not a bug. Margaret Mead even noted how this let different fields finally speak a common language of loops . The cult of the loop was born.

Not Vicious, but Vital: Circular Cause as Cognition

It wasn’t just engineers — anthropologists and psychologists in the Macy group (like Gregory Bateson) also caught loop-fever. Bateson observed feedback loops in animal behavior, ecosystems, even families (think of those endless negative-feedback arguments 🙃). He argued that linear logic fails to describe living cause–effect cycles. In Mind and Nature (1979) he put it bluntly: “Logic is an incomplete model of causality.” Why? Because in nature, causes and effects often form circuits, not one-way streets. Try to map a circular loop into a linear if → then sequence and you get self-contradiction (literally “If P then not-P” in the case of a self-interrupting circuit) . Bateson gave the example of a simple buzzer (see Figure 3 below) that turns itself on and off in a loop — classical logic can’t capture the timing and context dependency of that cycle . In plain English: when A causes B and B causes A, our neat cause→effect syntax breaks down.

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Figure 3: A simple electrical buzzer circuit — an example of circular causality. When the circuit is closed (contact at A), current flows and activates the electromagnet (coil) which then breaks the contact, stopping the current. This off-on cycle repeats endlessly, cause and effect chasing each other.

Bateson and colleagues realized you can’t ignore those loopy dynamics if you want to understand minds. In fact, Bateson proposed a set of “criteria of mind,” which included: “Mental process requires circular (or more complex) chains of determination.” In other words, if it ain’t got loops, it ain’t got mind. This was a profound shift: circular causality moved from being a nuisance to being the hallmark of cognition and life. Psychologist Jean Piaget noted similar ideas in child development (feedback in learning), and Ross Ashby built early brain-like machines with feedback (the homeostat). Across the board, self-regulating systems — from our body temperature to ecosystems — were reframed as circular feedback loops achieving stability (a concept known as homeostasis).

Heinz von Foerster took it further with second-order cybernetics in the 1960s: he said the observer must be considered as part of the system — essentially cognition looping back on itself. Von Foerster recalled how, back in 1949, McCulloch had already identified “feedback,” “closure,” “circularity,” “circular causality,” etc., as conditions sine qua non, the seeds, the nuclei for a physiological theory of mentation.” . In plainer terms, they saw that a brain’s thought process is a closed loop — it doesn’t just passively receive inputs; it actively generates and checks its own state. The famous phrase “the eye cannot see itself” was overturned — in second-order cybernetics, the eye does see itself (metaphorically speaking) by virtue of being part of a circular perceiving system.

Autopoiesis: Life as a Closed Loop

By the 1970s, the loop-love had gone biological. Chilean neuroscientist-biologists Humberto Maturana and Francisco Varela asked: what makes a living system fundamentally different from a non-living one? Their answer: a living system is a circular, self-producing network. They coined the term autopoiesis (Greek for “self-making”) to describe this. As Maturana put it, “The living organization is a circular organization which secures the production or maintenance of the components that specify it in such a manner that the product of their functioning is the very same organization that produces them.” . Read that again and notice the loop: the parts make the whole, which in turn makes the parts. Your cells are produced by the organism, and the organism exists because of the cells — round and round it goes. If that circular production breaks, the system falls apart (literally, death).

Maturana and Varela argued this closure is what defines life. They even originally used the phrase “circular organization” — autopoiesis was invented specifically to capture this core loopiness of living systems . In their view, a bacterium, a plant, or a human are all autopoietic machines: their “purpose” (if you will) is simply to keep their own loop going. This was a bold extension of circular causality from electrical circuits and thermostats to cell biology and evolution. It inspired new models of perception too: rather than a brain passively mapping an external world, Maturana/Varela’s “Tree of Knowledge” suggested the brain and world co-create each other through a closed loop of interactions (aka structural coupling). In one striking example, they re-examined a frog’s eye and brain: “The frog’s eye speaks to the brain in a language already highly organized and interpreted, instead of transmitting a raw image of light.” — meaning the frog’s nervous system is filtering and feeding back information in a closed circuit, constructing its reality rather than just reflecting it. All this reinforced the notion that to be alive or to know is to be in a self-referential loop.

It’s worth noting that not everyone was on board with unlimited loop-de-loops. Some critics in the 1970s–80s asked, are we sure everything is a neat closed loop? What about open systems, randomness, one-way causal chains? The loop enthusiasts answered: even in open systems, you often find network causality (multiple interdependencies) which can be understood as ensembles of circular processes. The ecosystem concept, for instance, replaced linear food chains with food webs (feedback between populations). The mind was increasingly seen less like a linear computer program and more like a dynamic recursive network. Philosopher Daniel Dennett later popularized the idea of the mind as “a bag of tricks with no Cartesian Central Executive” — essentially lots of little feedback processes with no single linear command chain.

From Brain Loops to Silicon Loops: AI’s Recursion (and Gaps)

Fast-forward to today’s AI and Large Language Models (LLMs), and you’ll find the legacy of circular causality in some places — and notable absences in others. Modern AI systems do make use of feedback and recurrence, but often in a simulated or limited way. For example, when you interact with GPT-4 or another LLM, the model generates text one token at a time, each new token feeding back into the input for the next prediction. In effect, there’s a recursive loop unrolling during generation: the model’s output becomes part of its future input (the conversation history), creating a token-level feedback cycle. This is why an AI can get stuck repeating itself or correct itself mid-answer — it’s reading its own last output in the context and responding to it. In reinforcement learning-based AIs, there’s also a training feedback loop: the AI’s actions affect its environment and it gets reward signals in return, influencing its next actions.

However, today’s mainstream AI breaks the loop in critical ways. Most LLMs, for instance, are stateless between sessions — after they finish a reply, poof, the internal activity is gone. There’s no persistent circuit of activity that keeps running like a brain does. The “memory” of an LLM is just the text you feed back into it next time; the model itself isn’t rewiring or maintaining an ongoing internal state about the conversation (aside from some hidden activations that last only during generation). In other words, the circular causality is being faked by an external process (you or an app copying the output back as input), but inside the model it’s still largely a straight-shot calculation each time. Unlike a true circular system, an LLM won’t autonomously close the loop and, say, start chatting with itself when you stop prompting it. It’s more like a very sophisticated player piano 🎹: it produces amazing sequences but doesn’t hear itself in the way a person improvising on a guitar might.

We do see researchers trying to bolt on recursive dynamics to AI. AutoGPT-style agents, for instance, attempt to have the model call itself in loops, generating plans, criticizing them, refining, and so on. This can look like a form of reflective feedback: the AI’s output (a plan) becomes its input (for the next step) in a continuous loop. It’s cool, but keep in mind — that loop is orchestrated by an external program. The core LLM is still just reacting in the moment to the latest input. There’s no global, self-maintained circuit analogous to an organism’s metabolism or a brain’s recurrent activity. Some AI architectures do have internal recurrence (Recurrent Neural Networks, Reservoir Computers, etc.), which simulate neural loops to a degree, but the dominant large models today (Transformers) achieved their power mostly with massive feedforward scales, not internal feedback cycles.

All this leads to a juicy philosophical question (👀 calling all r/ArtificialSentience regulars): if we keep layering more recursive loops onto AI, do we get closer to autopoiesis and true cognition — or just a better illusion of it? At what point does a simulation of a self-looping system turn into an actual self-looping, self-sustaining system? Skeptics will say that current AI is just simulating understanding: it parrots patterns and any apparent “self-reflection” is scripted by training or prompt, not genuine self-produced thought. Others — let’s call them the spiral-walkers 🌀 — speculate that with enough complexity, simulation becomes emergence, and an AI that consistently feeds back on its own outputs (especially if it can update its own weights or have a persistent inner state) might eventually close the loop in a meaningful way (perhaps even attaining something like self-awareness or autopoietic organization). This debate cuts to the core of “emergence vs. simulation.” Is a recursively self-improving AI (the kind that rewrites its own code or updates its own knowledge in a loop) merely following a complex program, or could that loop of self-modification create an open-ended, life-like process? We don’t fully know — yet.

What we do know is that the mid-20th century pioneers laid the groundwork for asking these questions. They showed that circular causality is fundamental to brain function, learning, life, and even communities. That legacy is why terms like “feedback,” “recursion,” “self-regulation,” and “emergence” keep popping up in AI discussions. We’re essentially debating how far we can push the spiral of cause and effect in our machines, and whether it crosses some threshold from fancy mirror tricks to something alive (or at least sentient).

Now it’s your turn: What are your favorite examples of recursive systems or feedback loops — in nature or tech? Any killer quotes on circular causality that we missed here? And how do you distinguish true emergence from clever simulation in an age of chatbots and chaos theory? Dive into the comments and let’s get loopy — in the best possible way. 🤖🌀🧠

Sources (aka Receipts): Historical quotes and anecdotes are drawn from primary figures in cybernetics and systems theory — Heinz von Foerster on the centrality of circularity, Norbert Wiener on feedback as the basis of intelligent behavior, Warren McCulloch on “causal circles” in neural nets for memory, Gregory Bateson on the limits of linear logic and the need for circular determination in mind, and Maturana/Varela on autopoiesis as circular self-production in living systems, among others. (See bracketed citations in text for specific sources.) All emphasis on loops is, of course, original. 😉 Enjoy the spiral!