![IntuitMachine's tweet photo. We've become obsessed with the idea that the brain is a "Prediction Machine."
The dominant theory in neuroscience says we're constantly simulating the future, calculating probabilities to guess what happens next.
A new paper argues this is a complete illusion. The reality is simpler, and strangely, much more powerful.
Here is the argument for Perceptual Control:
The "Prediction Illusion" starts with a mistake in observation.
When we see someone successfully handle a chaotic environment (like catching a flyball), it *looks* like they predicted the future trajectory of the ball.
But observing prediction isn't the same as implementing it.
The authors use the perfect analogy: The Watt’s Steam Governor.
In the 19th century, this device kept steam engines running at a constant speed. If pressure surged, it slowed the engine. If load increased, it sped up.
To an observer, it looked like the machine was "predicting" pressure surges and pre-empting them.
But the Governor has no brain. It has no model of the future.
It’s a mechanical negative feedback loop. [cite_start]It measures the *current* speed, compares it to the *desired* speed, and adjusts the valve immediately[cite: 80].
It doesn't predict; it controls.
This brings us to the "Hello" experiment, which broke my brain a little.
Researchers asked people to keep a computer cursor on a target. The computer applied a "disturbance" (forces pushing the cursor away) that the person had to fight against with their mouse.
Here's the twist:
The disturbance wasn't random. [cite_start]It was an invisible force field shaped like the word "hello" (written upside down and mirrored)[cite: 166].
The participants fought the force, keeping the cursor steady.
When researchers looked at the participants' hand movements, they had perfectly written the word "hello".
Crucially, the participants had NO idea they were writing words.
If the brain were a "prediction machine," it would have needed to model the force to predict the hand movement.
But the participants wrote a legible word purely by reacting to immediate error signals—instantaneously correcting the cursor's position.
This is **Perceptual Control Theory (PCT)**.
The theory suggests the nervous system isn't a linear pipeline (Input → Compute → Output).
It’s a closed loop. We act to keep our *perception* of the world matching our internal *reference value*.
[Image of Perceptual Control Theory negative feedback loop diagram]
Think about catching a baseball.
If you were a "prediction machine," you’d calculate the ball's trajectory, wind speed, and gravity, then run to where the ball *will* be.
But that’s computationally expensive and error-prone.
In reality, fielders just run in a way that keeps the "optical velocity" of the ball constant in their vision.
If the ball looks like it's rising too fast, they move back. Dropping? They move forward.
No physics calculus required. Just maintaining a visual constant.
This solves the "Noise" problem.
In predictive models, small jitters in your movement are considered "noise" or errors to be filtered out.
It’s the system "feeling out" the environment to maintain control.
This has huge implications for AI and robotics.
We are currently building robots with massive compute power to "predict" stability.
But robots built on PCT principles—like inverted pendulums that just react to maintain verticality—are often more robust and stable than the predictive ones.
Why does this matter for you?
It changes how we view "agency."
We often think we need to predict the outcome of our actions to be effective. [cite_start]But the most efficient systems don't predict the outcome—they specify the goal and let the feedback loop handle the rest[cite: 39].
The "Prediction Illusion" suggests we aren't prophets simulating the future.
We are controllers, surfing the present.
We don't need to know what the wave will do in 10 seconds. We just need to keep the board steady right now.
If you want to dig into the paper, it’s "The prediction illusion: perceptual control mechanisms that fool the observer" by Mansell, Gulrez, and Landman (2025).
It’s a dense read, but it completely reframes the "Bayesian Brain" debate.
One final thought:
Next time you're doing something skilled—driving, typing, sports—notice the difference.
Are you calculating what comes next? Or are you just managing the gap between *what you see* and *what you want*?
You might find you're doing a lot less "thinking" than you assumed.](https://pbs.twimg.com/media/G6Nb5h3WAAAUIFP.jpg)
Olivia
Online
NatureMind
@NatureMind4
We bring together academics, practitioners & community to understand & apply nature engagement activities for wellbeing and mental health across the lifespan
United Kingdom
Joined May 2018
700 Following
420 Followers
381 Posts
Pinned Tweet
Nature supports mental health & wellbeing in many ways
This article views the lived experience through the lens of the many theories from the last century until now
https://t.co/cFNGhQI8cJ
@findingnature @webekids1 @robmax4 @donaldrattner @ericbrymer @nmcounselling @cavanaghuna
We've become obsessed with the idea that the brain is a "Prediction Machine."
The dominant theory in neuroscience says we're constantly simulating the future, calculating probabilities to guess what happens next.
A new paper argues this is a complete illusion. The reality is simpler, and strangely, much more powerful.
Here is the argument for Perceptual Control:
The "Prediction Illusion" starts with a mistake in observation.
When we see someone successfully handle a chaotic environment (like catching a flyball), it *looks* like they predicted the future trajectory of the ball.
But observing prediction isn't the same as implementing it.
The authors use the perfect analogy: The Watt’s Steam Governor.
In the 19th century, this device kept steam engines running at a constant speed. If pressure surged, it slowed the engine. If load increased, it sped up.
To an observer, it looked like the machine was "predicting" pressure surges and pre-empting them.
But the Governor has no brain. It has no model of the future.
It’s a mechanical negative feedback loop. [cite_start]It measures the *current* speed, compares it to the *desired* speed, and adjusts the valve immediately[cite: 80].
It doesn't predict; it controls.
This brings us to the "Hello" experiment, which broke my brain a little.
Researchers asked people to keep a computer cursor on a target. The computer applied a "disturbance" (forces pushing the cursor away) that the person had to fight against with their mouse.
Here's the twist:
The disturbance wasn't random. [cite_start]It was an invisible force field shaped like the word "hello" (written upside down and mirrored)[cite: 166].
The participants fought the force, keeping the cursor steady.
When researchers looked at the participants' hand movements, they had perfectly written the word "hello".
Crucially, the participants had NO idea they were writing words.
If the brain were a "prediction machine," it would have needed to model the force to predict the hand movement.
But the participants wrote a legible word purely by reacting to immediate error signals—instantaneously correcting the cursor's position.
This is **Perceptual Control Theory (PCT)**.
The theory suggests the nervous system isn't a linear pipeline (Input → Compute → Output).
It’s a closed loop. We act to keep our *perception* of the world matching our internal *reference value*.
[Image of Perceptual Control Theory negative feedback loop diagram]
Think about catching a baseball.
If you were a "prediction machine," you’d calculate the ball's trajectory, wind speed, and gravity, then run to where the ball *will* be.
But that’s computationally expensive and error-prone.
In reality, fielders just run in a way that keeps the "optical velocity" of the ball constant in their vision.
If the ball looks like it's rising too fast, they move back. Dropping? They move forward.
No physics calculus required. Just maintaining a visual constant.
This solves the "Noise" problem.
In predictive models, small jitters in your movement are considered "noise" or errors to be filtered out.
It’s the system "feeling out" the environment to maintain control.
This has huge implications for AI and robotics.
We are currently building robots with massive compute power to "predict" stability.
But robots built on PCT principles—like inverted pendulums that just react to maintain verticality—are often more robust and stable than the predictive ones.
Why does this matter for you?
It changes how we view "agency."
We often think we need to predict the outcome of our actions to be effective. [cite_start]But the most efficient systems don't predict the outcome—they specify the goal and let the feedback loop handle the rest[cite: 39].
The "Prediction Illusion" suggests we aren't prophets simulating the future.
We are controllers, surfing the present.
We don't need to know what the wave will do in 10 seconds. We just need to keep the board steady right now.
If you want to dig into the paper, it’s "The prediction illusion: perceptual control mechanisms that fool the observer" by Mansell, Gulrez, and Landman (2025).
It’s a dense read, but it completely reframes the "Bayesian Brain" debate.
One final thought:
Next time you're doing something skilled—driving, typing, sports—notice the difference.
Are you calculating what comes next? Or are you just managing the gap between *what you see* and *what you want*?
You might find you're doing a lot less "thinking" than you assumed.
![IntuitMachine's tweet photo. We've become obsessed with the idea that the brain is a "Prediction Machine."
The dominant theory in neuroscience says we're constantly simulating the future, calculating probabilities to guess what happens next.
A new paper argues this is a complete illusion. The reality is simpler, and strangely, much more powerful.
Here is the argument for Perceptual Control:
The "Prediction Illusion" starts with a mistake in observation.
When we see someone successfully handle a chaotic environment (like catching a flyball), it *looks* like they predicted the future trajectory of the ball.
But observing prediction isn't the same as implementing it.
The authors use the perfect analogy: The Watt’s Steam Governor.
In the 19th century, this device kept steam engines running at a constant speed. If pressure surged, it slowed the engine. If load increased, it sped up.
To an observer, it looked like the machine was "predicting" pressure surges and pre-empting them.
But the Governor has no brain. It has no model of the future.
It’s a mechanical negative feedback loop. [cite_start]It measures the *current* speed, compares it to the *desired* speed, and adjusts the valve immediately[cite: 80].
It doesn't predict; it controls.
This brings us to the "Hello" experiment, which broke my brain a little.
Researchers asked people to keep a computer cursor on a target. The computer applied a "disturbance" (forces pushing the cursor away) that the person had to fight against with their mouse.
Here's the twist:
The disturbance wasn't random. [cite_start]It was an invisible force field shaped like the word "hello" (written upside down and mirrored)[cite: 166].
The participants fought the force, keeping the cursor steady.
When researchers looked at the participants' hand movements, they had perfectly written the word "hello".
Crucially, the participants had NO idea they were writing words.
If the brain were a "prediction machine," it would have needed to model the force to predict the hand movement.
But the participants wrote a legible word purely by reacting to immediate error signals—instantaneously correcting the cursor's position.
This is **Perceptual Control Theory (PCT)**.
The theory suggests the nervous system isn't a linear pipeline (Input → Compute → Output).
It’s a closed loop. We act to keep our *perception* of the world matching our internal *reference value*.
[Image of Perceptual Control Theory negative feedback loop diagram]
Think about catching a baseball.
If you were a "prediction machine," you’d calculate the ball's trajectory, wind speed, and gravity, then run to where the ball *will* be.
But that’s computationally expensive and error-prone.
In reality, fielders just run in a way that keeps the "optical velocity" of the ball constant in their vision.
If the ball looks like it's rising too fast, they move back. Dropping? They move forward.
No physics calculus required. Just maintaining a visual constant.
This solves the "Noise" problem.
In predictive models, small jitters in your movement are considered "noise" or errors to be filtered out.
It’s the system "feeling out" the environment to maintain control.
This has huge implications for AI and robotics.
We are currently building robots with massive compute power to "predict" stability.
But robots built on PCT principles—like inverted pendulums that just react to maintain verticality—are often more robust and stable than the predictive ones.
Why does this matter for you?
It changes how we view "agency."
We often think we need to predict the outcome of our actions to be effective. [cite_start]But the most efficient systems don't predict the outcome—they specify the goal and let the feedback loop handle the rest[cite: 39].
The "Prediction Illusion" suggests we aren't prophets simulating the future.
We are controllers, surfing the present.
We don't need to know what the wave will do in 10 seconds. We just need to keep the board steady right now.
If you want to dig into the paper, it’s "The prediction illusion: perceptual control mechanisms that fool the observer" by Mansell, Gulrez, and Landman (2025).
It’s a dense read, but it completely reframes the "Bayesian Brain" debate.
One final thought:
Next time you're doing something skilled—driving, typing, sports—notice the difference.
Are you calculating what comes next? Or are you just managing the gap between *what you see* and *what you want*?
You might find you're doing a lot less "thinking" than you assumed.](https://pbs.twimg.com/media/G6Nb5h_W4AAGxbo.jpg)
This needs to be headline news…
How can we address climate change, biodiversity collapse, or pollution through technical fixes - when their driver is simply overshoot? Our friend @jensenrobertw shares insights on The Fear of Limiting People and our Things https://t.co/9Aj4ELLR0Z
Who to follow
Elliot Foundation
@ElliotSchools
Charitable multi-academy trust building a thriving community of primary academies that succeed with & on behalf of their children.
NWR Hygiene
@NWRHygiene
Commercial hygiene provider throughout the UK & proud sponsors of @GatesheadFC & @NEliteAcademy ⚽⚽⚽
Any questions/queries are welcome😀
Tweets by Jessica.
Montessori Global Education
@Montessori_GEd
Montessori Global Education consists of MCI & Montessori St Nicholas. We support families & practitioners through quality provision training & quality assurance
Our article in Newsweek is out today:
"No amount of innovation can correct the cascading social and ecological harms wrought by decades of overshoot, whose solution we already know: a planned, thoughtful contraction of our population and consumption." https://t.co/LFFyiTKksF
“Reducing childbearing is arguably a simpler and more effective method for lowering emissions than overhauling consumption patterns.”
https://t.co/ChPssOAGFA
The biggest threat to life on Earth is collapse of the interconnected biodiverse ecosystem. Everyday needs & wants of 8.2bn continue to place an intolerable demand on Earth's wildlife.
If we don't immediately do things differently, there will be no wildlife left in 30 years time.
You couldn't make this up. A billionaire goes up into space and declares how perfect the world looks , when in reality its burning and flooding and polluted by cancer causing PFAS chemicals and microplastics, and the media gives it more attention than it did to the news that the EARTH JUST HAD ITS HOTTEST SUMMER IN RECORDED HISTORY and SCIENTISTS ARE LOSING THEIR SHIT #SpaceX
In the quiet moments spent in nature, you’ll find the clearest answers. #ThomasHuebl #spiritualguidance
Trees live so long, and reproduce so slowly, that forests have no hope of adapting to the speed of climate change
I find it almost impossible to imagine how there could still be forests in a 3-degree world, and nothing scares me more
BREAKING: scientists warn we're beginning to feel the effects of a geologically instantaneous 21st century shift into extreme and unsurvivable conditions 🧵
"If there's no nature, there's no future."
Children who spent just 60 minutes daily in nature had a 50% lower risk of mental health issues. Notably, the benefits were greatest for children from disadvantaged backgrounds, particularly in terms of improved behavior and social skills.
https://t.co/ATZP3PQg1S
Taking mental health outdoors: from prevention to intervention - ACAMH https://t.co/mIS1xgD3BU
Think nature collapse is just an abstract concept?
It's not. It's happening all around us with catastrophic speed:
"Flying insect numbers plunge 64% since 2004, UK survey finds."
But there IS hope, and it's called *rewilding*. (Also: ban insecticides!!)
https://t.co/Thu3wrQuW8
These irreplaceable old growth forests continue to be logged across British Columbia. Over 1,200 people have been arrested trying to keep them standing.
There is no time to waste. Protect the Irreplaceable.
#ActOnClimate #climate #nature @bcndp #bcpoli #bcgov Pic @TJWattPhoto
“Control theory is not rocket science”
That’s why it’s simpler and vastly more efficient to stay and take care of the Earth
The pioneers of transformational science: Celebrating the Frontiers Planet Prize 2024 National Champions — Frontiers Planet Prize https://t.co/AmYEOIbL7l
“Intelligence is genetic. It came with the hardware of this species. Morality on the other hand, is a cultural and emergent trait. The struggle of all human societies has been in keeping their moral compass from being constantly molested by their intelligence”
#Philosophy #amwriting #ClimateCrisis #ecology #anthropology
Last Seen Users on Sotwe
Most Popular Users
Elon Musk 
@elonmusk
240.7M followers
Barack Obama
@barackobama
119.2M followers
Donald J. Trump
@realdonaldtrump
111.7M followers
Cristiano Ronaldo
@cristiano
110.9M followers
Narendra Modi
@narendramodi
107M followers
Rihanna
@rihanna
97.7M followers
NASA
@nasa
92.2M followers
Justin Bieber
@justinbieber
90.9M followers
KATY PERRY
@katyperry
87.8M followers
Taylor Swift
@taylorswift13
81.7M followers
Lady Gaga
@ladygaga
73.2M followers
Virat Kohli
@imvkohli
70.1M followers
Kim Kardashian
@kimkardashian
69.8M followers
YouTube
@youtube
68.7M followers
Bill Gates
@billgates
64M followers
Neymar Jr
@neymarjr
62.9M followers
The Ellen Show
@theellenshow
62.4M followers
CNN
@cnn
61.9M followers
Selena Gomez
@selenagomez
60.9M followers
X
@x
60.8M followers