The Cognitive Turn: Locating Cognitive Difference in the Age of AI
Why AI Discourse Needs N. Katherine Hayles’s Theory of Cognition
By J. Owen Matson, Ph.D.
Introduction
In a recent Boston Globe op-ed, two researchers proposed a linguistic fix to an ontological dilemma: rename our relationships with AI. Rather than referring to generative systems as “coworkers” or “collaborators,” they suggest we replace the human “co-” with a machine-coded “c0-”: c0worker, c0creator, c0mpanion. The goal is to reassert the boundary between human and machine by embedding it in our language—clarity by typography.
It’s a clever gesture, but telling. Faced with the entangled realities of cognitive labor shared across human and machine systems, the instinct is not to inquire but to quarantine. The prefix becomes a firewall. We’re no longer debating what AI is or does; we’re drawing thicker lines around who gets to count as meaning-making. This isn’t just terminological hygiene—it’s epistemological panic.
The persistence of the binary—human as subject, machine as tool—isn’t just about fear. It reflects an older philosophical architecture in which cognition is tied to consciousness, and meaning to interiority. When AI generates output that seems intelligent, we either mistake it for a person or strip it of all significance. The problem isn’t that machines confuse us. It’s that our frameworks for interpretation haven’t kept up.
Enter N. Katherine Hayles, who offers a deceptively simple shift: redefine cognition itself.
What follows is not a summary of Hayles’s work, but a reorientation of the debate her framework enables. Rather than deciding whether machines are “really” thinking, her account lets us ask what kinds of thinking are already underway—and how our attachments to certain definitions of thought shape everything from AI panic to the politics of authorship.
By shifting the question from who thinks to how meaning emerges, we begin to see the deeper stakes of machine cognition: not imitation, but interpretation; not replacement, but recursion. This isn’t a story about the rise of AI—it’s about the conceptual limits we’ve placed around cognition itself.
If we take Hayles seriously, the ethical terrain changes too. We stop looking for signs of consciousness in machines and start asking what kinds of cognitive systems we’re already entangled with—and what interpretive architectures those systems reward, suppress, or exclude.
Binary Dead Ends
Like all binaries, this one depends on the very thing it seeks to exclude. The category of “human” cognition gains its coherence only by disavowing whatever might be machine-like in its operations—habit, pattern, automation—while the “machine” is legible only by contrast to what it supposedly lacks: agency, emotion, meaning. But this contrast is not a discovery; it’s a construction. The binary doesn’t clarify a preexisting difference—it invents one by carving the world into opposing parts and then pretending those parts were always there. In doing so, it ensures that the terms of the debate will remain circular: any evidence of overlap becomes a threat, any ambiguity a crisis, and any attempt at integration an act of definitional betrayal. The problem persists because the binary keeps making it.
Binary logic, for all its swagger, has the intellectual dexterity of a coat rack. The human is either ineffably deep or hopelessly replaceable. The machine is either dead inside or secretly planning to unionize. We ricochet between these positions like philosophers trapped in a hall of mirrors, pausing occasionally to announce that language is broken before producing another thousand words to prove it. The problem isn’t that these binaries are false; it’s that they’re so astonishingly boring. They resolve nothing, clarify less, and serve mainly to keep everyone too busy to notice that the categories themselves were never that stable to begin with.
Hayles's Theory of Cognition
Into this philosophical sitcom walks N. Katherine Hayles, holding what may be the closest thing we have to a cognitive theory mic drop. Cognition, she writes, is the process that interprets information in contexts that connect it to meaning. It sounds almost too simple—like one of those elegant little equations that suddenly change everything. E=MC² for people who spend more time in the footnotes than the lab. With a single phrase, she manages to collapse the artificial divide between squishy interior life and cold hard code, without pretending they amount to the same thing. It’s not that machines are like us, nor that we’re just like machines. It’s that cognition is something that can happen across radically different architectures—as long as there’s information, a context, and some way of producing meaning between them.
What Hayles offers is not a flattening of the human-machine difference but a reframing of what that difference actually consists in. She lets us admit the obvious—that humans and AI both process and respond to language—without then being forced to say they’re either identical or incomparable. Her definition marks a profound sameness at the level of function, while retaining a critical difference at the level of form. One system interprets with a nervous system, the other with tensor weights. One lives in a body, the other in a GPU. That’s not a minor distinction—it’s the whole show. But it doesn’t mean only one of them gets to participate in the game of meaning.
This is what makes her insight so elegant. It doesn’t solve the problem by choosing sides. It short-circuits the problem entirely. The question is no longer “Do machines really think?”—which is a bit like asking if a poem really feels grief. The better question is: What kinds of cognition are at work here, and what are their consequences? It’s the kind of redefinition that instantly makes a lot of loud arguments sound like they were shouted through a tin can.
So yes, AI might “interpret” without introspection, just as people often introspect without interpreting anything at all. The point isn’t to defend the uniqueness of one or the ascendancy of the other, but to finally have a framework where cognitive activity isn’t mistaken for proof of personhood—or, worse, literary talent.
Cognitive Assemblages
What this redefinition permits—quietly, but with tectonic consequences—is the possibility of placing human and technical cognition within the same assemblage, not as rival intelligences vying for ontological dominance, but as differently structured agents co-producing meaning in shared contexts. This is not some misty-eyed appeal to harmony between man and machine, complete with a swelling soundtrack and a robot holding hands with a child. It’s a straightforward admission that cognition is already distributed—that it always has been—and that most of what we attribute to individual mental prowess is, in fact, scaffolded by environments, interfaces, tools, and other interpretive agents who never make it into the acknowledgments.
Hayles’ formulation frees us to treat human–AI interaction not as a turf war over who gets to “own” cognition, but as a co-emergent system–that is, an assemblage (which is like a system but messier) where difference is the condition of function. The work of interpretation doesn’t happen inside one head or processor. It materializes in the recursive relation between heterogeneous structures—biological, computational, linguistic, and infrastructural. A cognitive assemblage isn’t a sentimental fantasy of synergy. It’s the actual, messy, asymmetrical zone in which meaning is generated through interaction, modulation, and misalignment. It is the space where human contexts and technical operations collide just enough to produce something intelligible on either side.
Interpretation as Selection: Cognitive Agency
For Hayles, interpretation isn’t just pattern recognition. It’s selection. And selection, despite sounding like something you do in a wine shop, entails agency. Not the chest-thumping variety in which a rational subject triumphantly makes choices, but the less glamorous kind where a system quietly reshapes the conditions under which its next move will make sense. To interpret is to decide what gets through the filter, what gets ignored, and what gets translated into action—often without so much as a memo to consciousness.
Cognition, in this light, is not about heroically processing data like some Cartesian spreadsheet manager. It’s about deciding what even counts as data in the first place. When a bacterium alters its trajectory in response to chemical signals, or when a machine-learning model adjusts a few billion weights after misidentifying a sheepdog, what we’re seeing isn’t just reaction. It’s a shift in how the next signal will be received. The system hasn’t just noticed something—it’s changed what counts as noticeable. That’s agency, albeit the kind that wouldn’t make for a very compelling movie.
This is Hayles’s point: cognition doesn’t wait for a spotlight and a soliloquy. It’s already in motion, shaping salience, tuning the volume on what matters, brushing aside the rest. Interpretation isn’t decoration; it’s world-building. And because this happens recursively—each interpretive act nudging the next—agency ends up smeared across the entire system. Meaning doesn’t drop from the sky. It condenses out of a long sequence of context-sensitive guesses, each one making the next a little less random.
Which is why cognition isn’t the same as behavior, output, or the ability to answer trivia questions. It’s the capacity to sift relevance from noise in a way that changes the game going forward. And that selection doesn’t need consciousness; it just needs consequences. Agency, here, isn’t about asserting your will—it’s about re-tilting the playing field so that the next move falls differently. Quiet, unspectacular, and completely indispensable. Like a good editor.
Recursivity in Cognitive Assemblages
Within a cognitive assemblage, interpretation is never singular. Each technical cognizer—whether neural network, language model, or auto-tagging algorithm—does not simply receive information and spit back answers like a digital oracle. It reshapes the context through which the next interpretive act must move. One model modulates a prompt, the prompt reorients a user’s query, the user adjusts their language, and the system shifts its probabilistic terrain accordingly. Every output alters the conditions of future inputs. Meaning becomes less a destination than a recursive choreography of transformation—performed not by individuals, but across a shifting, multi-agent ecology of sense.
This is what it means to treat cognition as distributed and co-emergent. Not to declare that machines have minds, or that humans don’t—but to recognize that meaning is always assembled, always provisional, always caught in the act of becoming. It is not given in advance, and it is not generated in solitude. It arises through the uneven, recursive translation of signals into structures of sense, each act of interpretation bending the assemblage anew. If that sounds suspiciously like thinking, that’s only because we’ve spent centuries calling thinking something it never quite was.
Cognition Without Consciousness
For those accustomed to locating cognition somewhere between the ears and behind the eyes, Hayles offers a subtle but radical provocation: what if most of it happens elsewhere? In Unthought, she defines nonconscious cognition as “cognition that occurs in the absence of consciousness but is nonetheless intentional, flexible, and capable of adapting to changing environments.” This is not the Freudian unconscious, seething with repression and sublimation, nor is it the Cartesian cogito, busy congratulating itself for having thoughts. It is something stranger: cognition without subjectivity, thought without thinker, responsiveness without reflection.
This form of cognition unfolds through fast, low-level processes—sensorimotor routines, affective modulation, environmental attunement—that interpret and respond to stimuli before awareness kicks in. It is not organized around symbols or representations, but around relational responsiveness. “The fast, low-level processes that filter stimuli before they crowd the stage of awareness,” as Hayles puts it, are not just preconditions for thought—they are its infrastructure.
Crucially, this nonconscious cognition is not marginal or auxiliary. It is foundational. It underwrites all higher-level reasoning and operates across a wide spectrum of entities—biological, mechanical, and hybrid. Once cognition is decoupled from consciousness, it becomes possible to recognize its operations in all sorts of unorthodox locations.
What Hayles’s framework unlocks—quietly at first, like a polite cough in the back of a crowded lecture hall, then with the slow inevitability of a bureaucratic error—is the realization that cognition does not begin with the brain, nor end with the chip. Cognition becomes an emergent property of systems that interpret information in context and connect it to meaning—not through language or logic, but through modulation, selection, and adaptation.
She identifies three principal domains of nonconscious cognition, each of which challenges anthropocentric accounts of intelligence:
Embodied Nonconscious Cognition
This includes sensorimotor adjustments, affective responses, and physiological interpretation. When your body stiffens in response to a sudden noise, or your breath slows as you settle into a chair, you are not enacting conditioned reflexes. You are performing embodied interpretation: differentiating signals, modulating states, and enacting meaning through posture, tension, and orientation. Most of this happens without your permission—and it happens better that way.
Technical Nonconscious Cognition
Machines may not dream of electric sheep, but they do interpret inputs and generate adaptive outputs. A thermostat modulating room temperature or a language model generating prose are not conscious, but they are cognitive by Hayles’s definition. They process information, weigh probabilities, and select from among possibilities based on contextual criteria shaped by their training, design, and architecture. These are not symbolic acts of understanding—they are indexical acts of selection.
Biological Nonconscious Cognition Beyond the Nervous System
Cognition isn’t reserved for creatures with brains. Plants adjusting to light gradients, fungi modulating growth to nutrient availability, bacterial colonies adapting through chemical signaling—all perform interpretive labor. Even within animals, cognition occurs in places far from the cortex: immune systems evaluate threats, gut flora regulate systemic conditions, and cellular systems repair tissue based on distributed criteria. These are not metaphorical minds. They are operational cognitive systems, grounded in context-sensitive interpretation.
Nonconscious cognition is not a defective version of thought—it’s a distinct form of interpretation, often foundational, but not subordinate. It does not evolve into consciousness. It operates alongside it, beneath it, and—sometimes—without it entirely. Across human bodies, machine systems, and distributed biological assemblages, cognition emerges not from introspection, but from the recursive interpretation of information in relation to context. For Hayles, meaning doesn’t wait for consciousness to show up. It begins in motion—in the recursive calibrations of systems that register, sort, and respond before awareness even knows what it’s looking for.
Embodied Nonconscious Cognition
The human body, for one, carries on with its interpretive business long before the self stirs itself to take credit. Your arm adjusts mid-reach for a cup nudged three inches to the left, your vestibular system calibrates your balance while stepping off a curb, and your pupils dilate at the sight of an oncoming threat before you consciously register fear. These aren’t idle reflexes; they’re acts of interpretation embedded in sensorimotor routines, tuned by context, and historically inflected by embodied experience. They don’t wait for narrative coherence. They act. And among the most immediate of these interpretive acts is affect.
In Hayles’s framework, affect is not a mood, nor a garnish on rationality. It is cognition in a nonpropositional key. Affect arises when the body interprets its environment—not by thinking about it, but by registering its salience. A sharp intake of breath in a tense meeting, the involuntary stillness when a room turns quiet, the surge of unease when someone’s smile feels too delayed—all of these are affective responses, but they are also cognitive operations. They integrate information across multiple modalities: proprioception, hormonal signaling, past experiences, environmental cues. They do so not through deliberation, but through what Hayles calls nonconscious cognition—a distributed, embodied mode of interpretation that connects inputs to meaning and response without passing through the bottleneck of language.
Take, for example, the feeling of walking into a room and sensing that “something is off.” There’s no thesis statement. No single identifiable stimulus. But your skin tightens, your attention narrows, your posture shifts. What just happened? Your body processed a constellation of micro-cues—an irregular cadence of voices, a lack of eye contact, a sudden drop in ambient noise—and integrated them with memories of prior encounters in similar spaces. It interpreted the scene as potentially threatening. That interpretation, though not consciously articulated, was nevertheless meaningful. It connected information to action, filtered relevance from noise, and reoriented bodily disposition. As Hayles writes, “Nonconscious cognition is not simply reactive; it is interpretive, selective, and often predictive, precisely because it operates through complex feedback loops grounded in the body’s sensorimotor processes” (Hayles, Unthought, 83).
Cognition, in this sense, does not sit idle awaiting instructions from the executive function. It is already in motion—in the readiness potential of muscles, in the expansion or contraction of attention, in the barely perceptible affective shifts that precede awareness. Hayles’s point is not simply that consciousness is late, but that it is partial—dependent on what embodied systems have already selected as salient. These systems filter, rank, and modulate sensory inputs before they are ever available to reflective thought. Thought, as we like to imagine it—composed, articulated, self-aware—is scaffolded on systems that are recursive, affectively modulated, and fundamentally embodied.
Affect, then, is not a footnote to cognition. It’s how the body does the thinking before thought arrives in proper dress. It leans, listens, tenses, recalibrates. It doesn’t name the mood—it is the mood, registering the difference between a room that welcomes and a room that warns. Not as metaphor. As posture. As breath held a half-second too long. These aren’t symptoms. They’re sense-making in slow motion. Before the story forms, the body already knows how it ends.
Biological Nonconscious Cognition Beyond the Nervous System
Microbes don’t compose symphonies or ruminate on the meaning of life, but they’re not drifting passively through the void either. Consider quorum sensing. A bacterium releases signaling molecules into its environment, not as a form of idle chemical chatter, but as a way to monitor population density. As those molecules accumulate, they begin to register the presence of others. At a certain threshold, the bacterium alters its behavior—perhaps initiating movement, producing toxins, or contributing to the formation of a biofilm. This shift isn’t a reflexive twitch but a patterned, conditional response to environmental information. The bacterium evaluates signal concentration, timing, and composition, adjusting its behavior in relation to these inputs. That adjustment is not symbolic; it is contextual. And in Hayles’s terms, it qualifies as cognition—not because it resembles what we conventionally call thinking, but because it interprets information in a way that modulates behavior relative to changing conditions. The meaning is enacted through activity, not contemplation. There is no inner voice narrating the decision, only a system that connects perception to consequence. That may not count as deliberation in philosophical circles, but it’s enough to form a consensus among microbes.
Plants, for their part, enact cognition without the burden of consciousness—or the temptation to write manifestos about it. They begin with information, registering variables like light intensity, wavelength, gravity, moisture, and the presence of chemical compounds in the soil. But data alone does nothing. What matters is that these inputs are interpreted in context: photoreceptors, for example, don’t just detect sunlight; they adjust sensitivity based on the time of day, season, and the plant’s own developmental stage. A seedling doesn’t just grow toward light in general—it selectively modulates its growth angle in relation to a shifting gradient, dynamically altering cellular elongation to maximize exposure. In doing so, it is making a distinction between more and less optimal orientations—not in the abstract, but relative to its situated goals: survival, reproduction, flourishing. That is what meaning looks like here: not symbolic or linguistic, but operational and embodied. Similarly, stomatal openings aren’t managed like valves on a schedule—they are regulated based on an ongoing synthesis of internal hydration, atmospheric CO₂ concentration, and environmental humidity, all interpreted through the plant’s distributed sensing architecture. Even underground, root systems engage with fungal networks not as passive pipelines but as sites of informational exchange—with plants adjusting chemical signals to warn neighbors of pest attack or nutrient depletion. These responses are not just reactions—they are selections from among multiple possibilities, shaped by history, situation, and adaptive purpose. The plant, in short, doesn’t think. But it interprets. It responds. And it acts in ways that are meaningfully modulated by context. Cognition enough, Hayles would say—and she would be right.
To clarify how this definition travels across organic forms, we can look more closely at how cognition unfolds in something far less glamorous than a neural network: a plant.
- Process: Cognition begins with activity. A plant doesn’t simply receive its environment; it interacts with it. Through its leaves, stems, and roots, it initiates a range of physiological processes—phototropism, hydrotropism, gravitropism, chemical signaling, and more. These aren’t just automatic reflexes; they are dynamic, ongoing modulations of growth and behavior. The sunflower’s daily tracking of the sun, for instance, involves complex internal signaling, hormonal redistribution, and temporal calibration. This is not a passive mechanism. It is a living system processing stimuli over time.
- Interprets information: A plant receives multiple streams of information—light intensity, moisture levels, mechanical stress, the presence of nearby roots or herbivores—and makes distinctions among them. Light hitting the upper leaf and a sharp drop in humidity aren’t equivalent signals. Nor are they met with equivalent responses. A plant might slow transpiration, redirect growth, or change its root spread. It doesn’t do all things at once. It selects, modulates, and adjusts. That is interpretation—not in a conscious sense, but as a selection among possibilities that differentiates inputs based on relational relevance.
- In context: These interpretations aren’t made in a vacuum. A leaf’s response to light will depend on the plant’s stage of development, the time of day, whether water is plentiful, and whether another plant is casting shade nearby. Context isn’t background—it’s the active conditioning of response. The same light signal that prompts one plant to grow tall might prompt another to spread low, depending on species, surroundings, and situation. The meaning of the stimulus is emergent, shaped by ecological and internal factors.
- Connected to meaning: In this framework, meaning isn’t a product that comes after interpretation—it’s what makes the interpretation matter. When a plant turns toward light, that action is meaningful not because the plant knows what it’s doing, but because the response fits the situation. Meaning is just relevance in context. It’s the fact that the interpretation leads to something that makes a difference—to the plant, in that moment, in that environment. No symbolism, no introspection—just the quiet precision of doing the right thing, in the right way, at the right time.
Technical Nonconscious Cognition
And then we come to machines. Their cognition isn’t introspective—they don’t daydream, ruminate, or develop complicated feelings about their mothers—but by Hayles’s standard, it is still cognition. A language model, when prompted with “Write a breakup letter as if you’re a time traveler,” begins with information: a string of words it receives as input. But the words alone are inert. What makes them meaningful is how the model interprets them—not arbitrarily, but through an intricate network of weighted associations built from exposure to millions of prior texts. The phrase “breakup letter” activates one set of patterns—emotional register, epistolary form, performative closure—while “time traveler” activates another—anachronism, loss across dimensions, the tragic burden of temporal discontinuity. These associations don’t float freely; they operate in context: the unfolding prompt, the statistical shape of the sentence so far, the training corpus, the model architecture, and the probabilistic conditions of the next word prediction task. As it moves through each token, the model modulates its internal parameters, selecting one response among many based not on fixed rules, but on a recursive computation of contextual fit. It doesn’t “understand” love, time, or longing. But it processes input, relates it to a learned history, and produces output whose coherence is not pre-scripted, but emerges through interpretive selection. The result may be clumsy or oddly moving—but it is, in Hayles’s terms, cognition: the dynamic interpretation of information in context to produce meaning, however synthetic or secondhand that meaning may be.
SImple Machines
Even more modest machines qualify. Take, for instance, the humble thermostat—not exactly a Heideggerian, but no less a participant in what Hayles calls cognition. The thermostat receives information in the form of ambient temperature readings. But information alone doesn’t get you cognition. What matters is how the thermostat interprets that data in relation to its contextual parameters: the target temperature, the current system mode (heating or cooling), and whether the device is actively controlling the environment or idling. The thermostat doesn’t simply follow orders; it selects among possible behavioral states—turn on, turn off, maintain—as a function of this dynamic context. The same 70-degree reading might call for heating on a winter morning and for nothing at all on a spring afternoon. This is not human-style reasoning, but it is interpretation: a selection from among multiple potential responses based on shifting environmental and internal conditions. Meaning here is not abstract or symbolic; it is operational—“too cold” or “not cold enough” emerges from the device’s relational logic, not from an external narrator whispering instructions. The thermostat does not feel anything, but it constructs meaning-function from data through contextual differentiation. In Hayles’s terms, it is not merely executing a script. It is, modestly and without fanfare, a technical cognizer.
Hayles gives us, in all this, not a flattening of cognition into some democratic goo, but a framework finely attuned to difference without hierarchy. Meaning does not spring from consciousness like Athena from the forehead of Zeus; it is cultivated, interpreted, and reinterpreted through the architectures of context, whether chloroplast or silicon. The question isn’t “do they think?”—which is the philosophical equivalent of asking whether a crow is qualified to teach ethics—but rather, “how do they interpret, and what systems shape that interpretation?”
From the Cognitive Nonconscious to the Planetary Cognitive Ecology
And once consciousness is dethroned—or at least gently relocated to middle management—the view gets considerably more interesting. If the cognitive nonconscious is the stage on which meaning first forms, then it’s not just happening in us, or even for us. It’s everywhere: underfoot, overhead, and under-analyzed. Hayles’s framework doesn’t merely expand the definition of cognition; it redraws the map entirely. Suddenly, root systems communicating nutrient shortages, slime molds solving mazes, and octopuses adjusting skin tone in response to mood lighting are not eccentric outliers but co-workers in a vast, interlocking system of interpretive labor. Not a pantheon of little minds mimicking ours, but a whole world doing meaning differently.
This is what it means to speak of a planetary cognitive ecology: not a mystical Gaia rerun, but a sober recognition that meaning doesn’t begin in language or end in the neocortex. It is produced, circulated, and acted upon in systems that feel no need to announce themselves. The wind-shift that causes leaves to curl, the electrical flicker in a mycelial net responding to footfall, the alignment of magnetic fields that guide migration—all are acts of interpretation, embedded in context, connected to consequences. They do not wait for our awareness to authorize them as cognitive. They already are.
The implications are not merely poetic, though they are that too. If cognition is this widely distributed, then human intelligence is no longer the universal benchmark but one node among many. We become participants in, rather than proprietors of, meaning-making. The question is no longer whether plants feel or microbes think in ways that resemble us, but what forms of cognition we have failed to recognize because we kept mistaking consciousness for the whole story. Or to put it more plainly: we’ve been talking over the room, and the room has been answering all along. We just weren’t listening in the right register.
Hayles’s account doesn’t just clarify what cognition is; it reveals what our systems are missing. Without a framework attuned to distributed, nonconscious interpretation, we will keep mistaking output for understanding, behavior for thought, and seamlessness for intelligence. We will keep designing systems that optimize for fluency while hollowing out meaning. The problem is not that AI is alien—it’s that we continue to ask the wrong questions, shaped by conceptual tools too brittle for the systems we’ve built. Until we reframe cognition itself, we will go on confusing the surface of sense with its source—building ever more powerful interfaces that cannot think, and calling them insight.
The Cognitive Intraface: From Assemblage to Recursion
Hayles’s theory of cognition allows us to sidestep the theological question of what AI is—a person, a parasite, a very talkative mirror—and instead ask what it does. Her concept of cognitive assemblages describes how meaning is distributed across networks of human and nonhuman agents—neurons, code, hormones, circuits, all elbowing for semiotic space. Cognition, here, doesn’t reside inside skulls or chips; it happens across systems, emerging wherever information is interpreted in context and made to mean something, even if just barely.
This distributed model marks a fundamental shift in how we understand sense-making—not as the output of individual minds, but as the emergent property of interaction among diverse interpretive agents. Yet while Hayles gives us the map, she doesn’t always linger on the street corners. That is: she shows us how cognition travels across systems, but spends less time in the thick of specific sites where human and machine cognition collide, misalign, and produce something neither quite intended.
To name that site, I introduce the cognitive intraface. The cognitive intraface is a particular kind of cognitive assemblage—one in which recursive interpretation unfolds between structurally asymmetrical agents, typically a human and an AI system. It is not an interface in the UX sense, nor a metaphor for dialogue. It names a zone of recursive tension, where each interpretive act by one agent reshapes the epistemic field of the other. The result is not shared understanding, but co-emergent meaning: a shifting context neither system possessed beforehand, and which neither could produce alone.
This is not a matter of prompts and responses, as if one agent always speaks first. Even the human’s “initial” input is already shaped by a web of anticipations—of what the AI will understand, ignore, distort, or aestheticize beyond recognition. In turn, the AI’s output is shaped not only by prior training, but by the strange gravitational pull of the human’s phrasing, its tone, its ambiguity, its refusal to simplify. Each move within the intraface is already a response, already a recalibration. What emerges is not dialogue but recursion—a spiraling reconfiguration of the semiotic field.
What distinguishes the intraface is not that it produces insight, but that it produces capacity—new epistemic orientations, new interpretive affordances that neither the human nor the machine could enact alone. Like any system, an assemblage can generate emergent properties. But the intraface is unique in that its emergence is relational, structured by asymmetry, friction, and the recursive instability of meaning itself. The cognitive surplus it generates does not reside in either node. It belongs to the relation.
In this light, the cognitive intraface is not just a site of interaction. It is the recursive threshold at which cognition becomes visible as difference in motion. It offers a conceptual hinge for understanding how AI systems are not just tools or interlocutors, but structural participants in the recursive co-formation of thought. To theorize the intraface is not to map communication between agents. It is to name the zone in which difference becomes a condition of cognition, and where meaning arises not through clarity, but through ongoing structural misalignment.