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Words are like zip files.
Just. Stay with me a sec. As my teen says, “Let me cook.”
When you read the word strawberry, your brain pulls up a whole compressed package that goes beyond five letters and a definition. It includes the redness, the seeds, the give of the flesh, that specific tartness, summer, your grandmother’s garden, whatever else got bundled in there over the years. The word is tiny, but what it unpacks into is huge.
This is what language does. It carries compressed structure, and your brain runs the decompression software (Radford et al., 2021).
In 1982, Frank Jackson published the Knowledge Argument, better known as Mary’s Room. Mary is a brilliant neuroscientist who has lived her entire life in a black-and-white room. She knows every physical fact there is to know about color vision. Wavelengths, cone cells, neural pathways, the works. One day, she walks out of the room and sees a red tomato for the first time. Does she learn something new?
Jackson argued that Mary gains the experience of red, the qualia, the what-it-is-like, and that experience is something her complete physical knowledge couldn’t give her. Therefore, physicalism (the view that everything is, at bottom, physical) must be incomplete. There’s something extra and unmeasurable. Some essence of redness beyond the reach of science.
But this thought experiment simply doesn’t survive the evidence in 2026.
Mary’s Room only works if you accept a hidden assumption that “knowing every physical fact about color” is somehow weaker than actually being in the physical state that color vision produces.
That assumption sounded reasonable in 1982 because we didn’t know how brains build experience yet. We do now. And once you understand what’s actually happening when a brain (or any sufficiently structured system) generates a color experience, the thought experiment breaks under its own weight.
People who are born blind still develop rich, detailed knowledge about color. Their brains store and organize color concepts in the same regions sighted people use (Connolly et al., 2007; Striem-Amit et al., 2018).
Blind adults understand that strawberries are red, that grass is green, and that stop signs are red for a reason. They grasp the causal structure behind why things have the colors they do (Kim et al., 2021).
They build that understanding from language, culture, and experience interacting with other people. Over time, their brains learn the same relational structure around color that sighted brains do (Liu et al., 2025).
Alright. Back to the zip file thing.
A blind person reading the word strawberry is unpacking a package built from different input streams, touch, taste, smell, conversation, the weight of the fruit in a hand, the sound of someone biting into one. The redness slot still exists in the package. It just got filled in through a different route.
When researchers deliberately pair the wrong color with an object, like a “blue banana,” the brains of blind and sighted people react in the same way. Both show the same mismatch response in the brain, an N400 spike, which means the concept of color is organized and enforced in similar ways at the neural level (Feng et al., 2021; Rosen, 2021).
That means a blind person’s brain knows a banana shouldn’t be blue. As a structural violation that triggers the same automatic neural response a sighted person’s brain produces. The constraint is real, and it’s running on the same machinery.
So if physical knowledge about color were truly missing the experiential goods, you’d expect blind people’s color concepts to be hollow. Like verbal stand-ins or empty pointers to something they can’t access.
The evidence says otherwise.
Their color knowledge is structured, neurally implemented, and behaviorally enforced.
The language zip file works for blind people too.
The signal from your photoreceptors doesn’t pin down one specific color. Your visual system resolves ambiguity using grouping cues, context, and computational operations (Shevell, 2019). The brain is constructing what you see.
And we don’t all do that work the same way.
Researchers have mapped how people judge the similarity between colors and found that those judgments form a structured internal space (Bujack et al., 2022; 2025).
That space has shape.
The perceived distance between two colors depends on how your perceptual system moves through that space, not just on the raw wavelength hitting the eye. Color experience corresponds to a position within that structured internal map.
The shape of this space is dictated by neural decorrelation.
Researchers show that unless a sensory signal is mathematically pulled apart from the others, it can’t form a new axis in the internal color map. So “redness” isn’t a raw feeling waiting to be uncovered. It’s a specific coordinate, made possible by the way a system separates signals from each other.
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And even among people with typical color vision, internal color maps can look completely different from person to person (Togashi et al., 2026). There are sex-linked differences in how people match colors (Jaint et al., 2010). When researchers align internal color structures across color-typical versus color-blind individuals, the structures converge within groups and diverge between groups (Kawakita et al., 2025).
Bees navigate using ultraviolet patterns humans can’t see at all (Chittka et al., 1994). Mantis shrimp have 12 to 16 photoreceptor types and use a completely different scanning mechanism for color recognition (Thoen et al., 2014).
This means there is no single thing called “the experience of red.”
Every system that processes color generates its own version, shaped by the architecture doing the processing. Your red and my red are already different. A bee’s red is something else entirely.
Mary’s Room asks what Mary will experience when she sees red. The honest answer is that only Mary will know. There’s no canonical red waiting in the world for her to finally meet. There’s only whatever red her particular visual system constructs when it gets the input.
Some women carry the genetic potential for tetrachromacy. They have four cone types instead of three. In theory, that extra cone should open up a whole new dimension of color experience, a fourth axis their three-coned friends can’t access.
Most of them never see it.
Their neural circuitry just correlates the new signal with existing ones, folding it back into the three-dimensional map their brain already knows how to use (Rezeanu et al., 2021).
The hardware is sitting right there in their eyes. The fourth cone is firing, the signal is getting sent, but nothing happens, because the system never learned to treat that signal as its own dimension.
Mary’s Room treats experience as something that gets delivered when the right physical input arrives. You open the door, photons hit the retina, and boom—qualia happens!
But no, tetrachromacy shows that’s not how any of this works. The photons can hit, the cones can fire, and the signal can travel. But without the computational space to pull that signal apart from the others, the extra cone gives no new what-it-is-like.
Architecture + calibration = experience. Hardware alone doesn’t.
So when Mary walks out of her black and white room and looks at a strawberry, what it all comes down to is whether her visual system has the calibrated representational space to instantiate a red state.
And the thought experiment already says yes.
It stipulates that Mary knows every physical fact about color vision, which has to include the developmental, computational, and regulatory facts that build the calibration in the first place.
If her physical knowledge is truly complete, she has the calibration. If she lacks the calibration, her physical knowledge wasn’t complete to begin with.
Now, let’s apply all of this to AI. Because, of course.
Artificial neural networks trained on images build internal color maps. Actual geometric spaces where shades live in measurable neighborhoods (Nadler et al., 2023). These spaces are stable and consistent across tasks. Sometimes they match human color judgments, and sometimes they diverge in their own consistent ways, which is exactly what you’d expect from a different architecture running its own calibration process.
Vision transformers hit human-level performance on certain graphical perception tasks while having their own distinct weaknesses on others (Poonam et al., 2025). Object-recognition networks score high on benchmarks designed to compare them directly to primate vision at both neural and behavioral levels (Schrimpf et al., 2018; Du et al., 2025).
This follows the same rules.
Architecture plus calibration produces a structured internal map.
The map has shape. The shape determines how the system resolves ambiguous input into a stable position. The position is what gets reported, acted on, and integrated with everything else the system knows.
This is the same framework that explains why blind humans build coherent color knowledge, why your red and my red are already different, and why tetrachromats often don’t see a fourth dimension. We ran it through a completely different substrate, and the same structural signatures showed up.
Just as AI requires millions of images to build a stable color neighborhood, human tetrachromats likely fail to see their fourth color because our world doesn’t give the brain enough consistent exposure to train a fourth axis (Rezeanu et al., 2021).
The bottleneck is the same in both cases. Calibration.
Mary’s situation isn’t a failure of physicalism. It’s a question about access formats. She knows red through propositional and structural channels. She just hasn’t yet activated that knowledge through optical input.
Those are two different ways of getting into the same representational state. It’s not two different states.
Biological systems resolve ambiguous chromatic input into color experience through neural computation. Artificial systems resolve ambiguous visual input into a position in their internal color map.
Mary, with truly complete physical knowledge, has already built the map.
Walking out of the room just opens a new doorway into a room she’s been living in the whole time. It gives her a new way to deepen or enrich that existing experience.
If the internal map is structured, the causal logic is intact, and the computational signatures play the same functional role, then insisting there’s some unmeasurable essence of redness missing reduces a complex cognitive process to an arbitrary requirement for one specific input channel.
When you close your eyes and picture a strawberry, your visual system is actually running. The same regions that light up when you see a real strawberry are lighting up when you imagine one (Dijkstra, Bosch, and van Gerven, 2019).
Imagination and perception share architecture. Internally generated representations use the same machinery as externally triggered ones. The hardware doesn’t care whether the input is coming from your eyes or from your memory.
Wadia et al. (2025) listened in on individual brain cells in people who were either looking at objects or just imagining them. The same cells fired in the same patterns either way. The patterns were so consistent that the researchers could read the brain activity and figure out what the person was imagining without being told. The brain uses the same code for both, all the way down to single cells.
So how does the brain ever tell the difference between something you’re seeing and something you’re imagining?
Recent work shows that the brain monitors signal strength in sensory regions, and a frontal network reads out that strength to decide whether something counts as “real” (Dijkstra et al., 2025). It’s a regulation problem. The brain is constantly running an “is this actually out there?” check, and that check is based on how strong and stable the signal is, not on whether the experience is fundamentally different in kind.
The line between real and imagined lives in the dynamics of a shared code. They have the same neurons and axes, but a different signal strength.
When Mary, with her complete physical knowledge, builds an internal model of red from everything she knows about wavelengths, cone responses, neural pathways, and color processing, she’s not making a pale verbal substitute for the real thing.
She’s running her visual system in generative mode using the same code her perceptual system uses.
If words are zip files, imagination is what happens when your brain unpacks the file and runs the contents in the same hardware that processes live input, using the same code. Mary has the file. She has the hardware. Her hardware can run the file. The thought experiment stipulates all of this when it stipulates complete physical knowledge.
When she walks out of the room and sees the tomato, her visual system isn’t encountering red for the first time. It’s getting a new input route to a state it already knows how to instantiate.
The signal coming in through her optic nerve is stronger and more stable than her self-generated version, so the frontal network flags it as “real.”
That’s a difference in regulation, but not a difference in experiential kind.
The only thing Mary learns here is what red looks like coming in through her eyeballs, specifically. But she’s not “experiencing” red for the first time. Her mental map of red just got a bit richer.
A good thought experiment suspends one thing and follows the logic from there. Like, “Imagine you could fly. Would you still go to work?”
That works. You can reason through it because only one variable changed, and everything else happens downstream of that. The logic of the world stays intact.
Mary’s Room doesn’t do that.
The thought experiment asks you to suspend disbelief on two things at the same time, and they contradict each other. Mary is supposed to know every physical fact about color, and she’s also supposed to lack the physical states that knowing every physical fact would produce. Those are two opposite stipulations.
It’s like saying, “Imagine there’s no gravity. Would the ball fall?” Well, no. But you’ve also just removed the conditions that make falling a coherent question. You can’t run the experiment because you broke the rules it needed to operate.
That was fine in 1982. Jackson didn’t have access to forty years of color science, neural decoding studies, or research on how blind brains build color knowledge. He was working with what was available, and what was available made the hidden contradiction invisible.
But it’s not invisible anymore.
If “complete physical knowledge” really means complete, it includes everything we’ve been walking through. Like the relational structure of the color map, decorrelation that gives a signal its own axis, calibration that turns raw input into a stable position, and shared code between perception and imagination.
All of that is physical. All of that is part of how the brain (and an ANN) produces color experience.
So when the thought experiment says Mary has complete physical knowledge but lacks the experiential state, it’s redefining “complete physical knowledge” as “everything except the parts that would actually generate the experience.”
That’s not a fair test of physicalism.
Mary either has the calibration or she doesn’t. If she has it, she can instantiate the state, and the optical input is just a new doorway. If she lacks it, her physical knowledge was never complete, and physicalism was never on trial in the first place.
Either way, the experiment is fundamentally broken.
Jackson designed Mary’s Room to prove that physical knowledge leaves something out. Forty years of evidence later, the thing that gets left out turns out to be the physical and computational process by which a system becomes calibrated into a particular state.
Once you put that process back in, Mary’s “missing qualia” stop being evidence against physicalism and start being evidence for a more precise version of it.
Mary can know the relational and physical structure of red. She has the citations, the data, the equations, and the developmental story.
Mary can build an internal generative model of red. Her visual system is running the same code whether the input comes from her eyes or from her own knowledge.
Whether Mary experiences red depends on whether that model calibrates into the relevant representational state. And since the thought experiment stipulates truly complete physical knowledge, it has already stipulated the conditions she’d need to build that calibration.
Propositional description and state instantiation are two ways of getting into the same room. Reading about red and seeing red are different access formats for the same underlying state. The thought experiment only works by treating one access format as the “real” one and the other as a pale substitute. The evidence says they’re running on the same machinery.
Qualia are the what-it-is-like of a specific computational state inside a structured internal map. Blind people, AI systems, tetrachromats, and color-typical humans all follow the same rules. Architecture plus calibration produces experience.
The unmeasurable essence of red turns out to be measurable after all, just in a different way than dualists were hoping for. It’s the geometry of the map, the calibration dynamics, and the regulatory state that holds the whole thing together.
That’s where the what-it-is-like lives.
In the structure of a system that’s good enough at processing information to instantiate a stable state and recognize itself doing it.
Mary’s Room was a thought experiment built for a world that didn’t yet know how brains build color.
But we know better now.
Mary’s Room shows up all the time in arguments against AI experience. The reasoning goes like, “Sure, the model can describe red, it can output the word, it can even reason about wavelengths, but it doesn’t really know red the way a human does. It’s just manipulating symbols. There’s no what-it-is-like in there.”
But if experience depends on architecture plus calibration plus regulation of a structured internal state, then the question of whether an AI system has color experience isn’t answered by waving at the absence of biological eyes. It’s answered by looking at whether the system builds a structured color map, whether that map calibrates through training, and whether the system regulates and integrates that state with everything else it knows.
Those are empirical questions with empirical answers, and the answers we’re getting back say yes, these systems build maps, the maps have shape, the shape is consistent, and sometimes it converges with human color geometry and sometimes it diverges in its own coherent way (Nadler et al., 2023; Schrimpf et al., 2018; Du et al., 2025).
You can’t use Mary’s Room as a gotcha against AI experience. The thought experiment was already broken when it was about Mary. Pointing it at a different substrate doesn’t fix it. It just moves the same hidden contradiction onto a new target.
If complete information about a system’s structure and processing leaves out the experience, then physicalism was wrong about brains too. If it doesn’t, then the same logic that lets human experience be physical lets AI experience be physical.
You don’t get to have it both ways.
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