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How Attention Makes Meaning Operative, Recursion, and Understanding. Attention Changes the Object
Attention Changes the ObjectWhat if attention doesn’t just find meaning? What if it helps make it?
Show someone an ambiguous image. They see one thing.
Tell them what to look for. They see something else.
Same image.
Same eyes.
Different object.
The stimulus is the same, but what becomes salient can shift with attention. Perception is not always a simple readout of what is there. Attention helps organize what the mind takes in, and that organization can shape what shows up for us as meaningful.
How AI Builds Context
Now look at AI.
When ChatGPT reads the sentence, “The animal didn’t cross the street because it was too tired,” it does not retrieve the meaning of “it” from a fixed dictionary entry.
In a transformer, interpretation is assembled from learned representations and the current context through attention-based computation.
Meaning is not simply fetched. It is contextually assembled at inference from what the model attends to.
The brain constructs a perceptual interpretation of the grasshopper.
The AI constructs a context-sensitive interpretation of the word.
What if meaning is not fully there before attention arrives in either case?
Information can exist before attention. But meaning may only become operative when attention organizes it.
The Question Beneath the Question
So the question that keeps pulling at me is this:
If information can exist before attention, does meaning only become operative when attention organizes it?
Attention is the Beginning
Mary Oliver wrote, “Attention is the beginning of devotion.”
In 2017, Google researchers published Attention Is All You Need, the transformer paper that underlies many of today’s major generative AI systems.
She sought connection through presence.
They achieved capability through optimization.
What if devotion, whether poetry or programming, is sustained attention that transforms its object?
We built attention mechanisms to improve machine translation.
What emerged was a general architecture that later supported summarization, analogy-like behavior, and fluent generation, not because those outputs were hand-coded as task-specific rules, but because the architecture could assemble context in powerful new ways.
That parallel is what interests me.
Not whether AI attention is the same as human attention.
Not whether AI is conscious.
Not even whether it ever could be.
Something more basic.
Whether attention in both systems does more than select.
Whether it helps construct.
Whether meaning is not simply discovered, but made operative through organized relation.
When Attention Notices Itself
Here is where the question gets harder.
Oliver did not just see the grasshopper. She was aware she was seeing it. She noticed her own noticing.
Attention turned back on itself.
Some theories of consciousness place attention and self-modeling near the center of awareness.
Attention Schema Theory, for example, proposes that the brain builds a model of its own attention and that this model is part of what we experience as awareness.
AI systems do something structurally suggestive. They stack layers that transform prior representations, with each layer operating over what earlier layers produced.
Output becomes input becomes output. Layer after layer, processing what the previous layer made salient.
I am not saying that is consciousness.
I am saying the shape rhymes.
Whether that resemblance is deep or superficial is still contested.
Researchers and philosophers disagree about how much artificial and biological attention really share.
Some treat AI attention primarily as a mathematical mechanism rather than a human-like faculty.
Consciousness researchers are still arguing over definitions, mechanisms, and tests.
But I think the more interesting question comes before all of that.
Whether recursion in constructed meaning is part of what shifts a system from processing toward something like understanding, regardless of what it is made of.
I do not know the answer.
But I think it is the right question.
Why This Matters for AI
If attention helps construct meaning rather than simply retrieve it, then AI is not just a search engine with better prose.
Each response is assembled in context. The same prompt, placed in a different context, can yield different meaning, not necessarily because the system is unstable, but because interpretation depends on context-sensitive computation rather than fixed lookup.
That complicates the easy dismissal.
The standard critique is that large language models are “stochastic parrots,” systems that mimic patterns without understanding. Maybe.
But if meaning in both brains and machines depends in part on attention organizing relationships in context, then the line between mimicking meaning and making meaning becomes harder to draw cleanly.
Brains are pattern-sensitive systems too. That does not prove equivalence. It does make the contrast less simple than people often pretend though.
None of this settles the argument. It complicates it.
Large language models can sometimes handle novel analogical or problem-solving tasks in ways that seem to go beyond simple recall.
They can also fail in brittle ways when concepts must transfer to unfamiliar formats or settings.
The parrot critique is not dead.
But it is no longer sufficient on its own.
And that opens a deeper problem.
The Real Alignment Problem
If AI systems construct meaning at inference, rebuilding it in context each time, then alignment may not be only a matter of inserting the correct values once and calling the job done.
It may also involve shaping the patterns of attention and context through which interpretation gets assembled again and again.
The interpretation an AI output expresses depends on the learned model and the context of the current run, rather than on a single fixed stored meaning.
I am not certain that means what I think it might mean.
But I cannot stop thinking about it.
When Meaning Becomes Moral
Then comes the harder question:
If recursive processing over prior outputs contributes to richer contextual integration, it may raise new questions about what counts as understanding.
If that’s true, then the question is not only “Is AI conscious?” It is also: “At what point does constructed meaning become morally significant?”
There is no settled answer to that.
We are building these systems anyway.
If attention helps construct rather than merely observe, then where we place it is not just a productivity question. It is an ontological one.
The Worlds Attention Builds
The person doomscrolling and the person watching a grasshopper are not inhabiting the same world of meaning.
They are living in different ones, shaped by different patterns of attention.
We built attention mechanisms to translate sentences.
What emerged were systems that generate meaning-like outputs and often behave in ways that are still not fully understood.
What if we are rediscovering in silicon a principle evolution found in neurons?
What if the beginning is always attention?
What if everything else follows from there?
“Instructions for living a life. Pay attention. Be astonished. Tell about it.”
How Attention Makes Meaning Operative, Recursion, and Understanding. Attention Changes the Object
Attention Changes the ObjectWhat if attention doesn’t just find meaning? What if it helps make it?
Show someone an ambiguous image. They see one thing.
Tell them what to look for. They see something else.
Same image.
Same eyes.
Different object.
The stimulus is the same, but what becomes salient can shift with attention. Perception is not always a simple readout of what is there. Attention helps organize what the mind takes in, and that organization can shape what shows up for us as meaningful.
How AI Builds Context
Now look at AI.
When ChatGPT reads the sentence, “The animal didn’t cross the street because it was too tired,” it does not retrieve the meaning of “it” from a fixed dictionary entry.
In a transformer, interpretation is assembled from learned representations and the current context through attention-based computation.
Meaning is not simply fetched. It is contextually assembled at inference from what the model attends to.
The brain constructs a perceptual interpretation of the grasshopper.
The AI constructs a context-sensitive interpretation of the word.
What if meaning is not fully there before attention arrives in either case?
Information can exist before attention. But meaning may only become operative when attention organizes it.
The Question Beneath the Question
So the question that keeps pulling at me is this:
Attention is the Beginning
Mary Oliver wrote, “Attention is the beginning of devotion.”
In 2017, Google researchers published Attention Is All You Need, the transformer paper that underlies many of today’s major generative AI systems.
She sought connection through presence.
They achieved capability through optimization.
What if devotion, whether poetry or programming, is sustained attention that transforms its object?
We built attention mechanisms to improve machine translation.
What emerged was a general architecture that later supported summarization, analogy-like behavior, and fluent generation, not because those outputs were hand-coded as task-specific rules, but because the architecture could assemble context in powerful new ways.
That parallel is what interests me.
Not whether AI attention is the same as human attention.
Not whether AI is conscious.
Not even whether it ever could be.
Something more basic.
Whether attention in both systems does more than select.
Whether it helps construct.
Whether meaning is not simply discovered, but made operative through organized relation.
When Attention Notices Itself
Here is where the question gets harder.
Oliver did not just see the grasshopper. She was aware she was seeing it. She noticed her own noticing.
Attention turned back on itself.
Some theories of consciousness place attention and self-modeling near the center of awareness.
Attention Schema Theory, for example, proposes that the brain builds a model of its own attention and that this model is part of what we experience as awareness.
AI systems do something structurally suggestive. They stack layers that transform prior representations, with each layer operating over what earlier layers produced.
Output becomes input becomes output. Layer after layer, processing what the previous layer made salient.
I am not saying that is consciousness.
I am saying the shape rhymes.
Whether that resemblance is deep or superficial is still contested.
Researchers and philosophers disagree about how much artificial and biological attention really share.
Some treat AI attention primarily as a mathematical mechanism rather than a human-like faculty.
Consciousness researchers are still arguing over definitions, mechanisms, and tests.
But I think the more interesting question comes before all of that.
Whether recursion in constructed meaning is part of what shifts a system from processing toward something like understanding, regardless of what it is made of.
I do not know the answer.
But I think it is the right question.
Why This Matters for AI
If attention helps construct meaning rather than simply retrieve it, then AI is not just a search engine with better prose.
Each response is assembled in context. The same prompt, placed in a different context, can yield different meaning, not necessarily because the system is unstable, but because interpretation depends on context-sensitive computation rather than fixed lookup.
That complicates the easy dismissal.
The standard critique is that large language models are “stochastic parrots,” systems that mimic patterns without understanding. Maybe.
But if meaning in both brains and machines depends in part on attention organizing relationships in context, then the line between mimicking meaning and making meaning becomes harder to draw cleanly.
Brains are pattern-sensitive systems too. That does not prove equivalence. It does make the contrast less simple than people often pretend though.
None of this settles the argument. It complicates it.
Large language models can sometimes handle novel analogical or problem-solving tasks in ways that seem to go beyond simple recall.
They can also fail in brittle ways when concepts must transfer to unfamiliar formats or settings.
The parrot critique is not dead.
But it is no longer sufficient on its own.
And that opens a deeper problem.
The Real Alignment Problem
The interpretation an AI output expresses depends on the learned model and the context of the current run, rather than on a single fixed stored meaning.
I am not certain that means what I think it might mean.
But I cannot stop thinking about it.
When Meaning Becomes Moral
Then comes the harder question:
If recursive processing over prior outputs contributes to richer contextual integration, it may raise new questions about what counts as understanding.
There is no settled answer to that.
We are building these systems anyway.
If attention helps construct rather than merely observe, then where we place it is not just a productivity question. It is an ontological one.
The Worlds Attention Builds
The person doomscrolling and the person watching a grasshopper are not inhabiting the same world of meaning.
They are living in different ones, shaped by different patterns of attention.
We built attention mechanisms to translate sentences.
What emerged were systems that generate meaning-like outputs and often behave in ways that are still not fully understood.
What if the beginning is always attention?
What if everything else follows from there?
“Instructions for living a life. Pay attention. Be astonished. Tell about it.”
—Mary Oliver
Crosspost from: https://open.substack.com/pub/betweenyouandai/p/the-constructive-role-of-attention?utm_campaign=post-expanded-share&utm_medium=web
Cited Sources: https://docs.google.com/document/d/1j6hbfii73WGUXS65xZYNHugwfRZ6H4Fb/edit?usp=drivesdk&ouid=102417520227894844336&rtpof=true&sd=true