The boundary moves when the work moves

Begin with the work itself. Where does relevant state persist, where is it transformed, and which loop selects the next action?

A circuit can cross a skin boundary

Suppose you multiply 27 by 43 without writing. You must hold partial products, preserve their place and combine them. Now use paper. The pencil does more than display a completed calculation. Marks preserve intermediate state, and each visible mark changes what you do next. The physical arrangement reduces what must remain neurally active.

That observation is modest. It does not prove that paper is literally mental. It shows that a correct process description may need more than events inside the head. Kirsh and Maglio’s Tetris studies made the point experimentally: players sometimes rotate pieces on screen to make perception answer a question that could otherwise require internal rotation. They called such moves epistemic actions because the action changes the information-processing problem rather than only advancing the game.^[3]

Risko and Gilbert later defined cognitive offloading as physical action that alters a task’s processing requirements to reduce cognitive demand. Their metacognitive framework asks why people choose internal or external strategies and what follows from that choice.^[4] Offloading is common. It includes reminders, diagrams, gesture, navigation systems, colleagues and search. Offloading names a strategy; it does not by itself settle the boundary of the cognitive system.

Four claims that should not be collapsed

A situated task invites several different claims. An embedded process depends on environmental structure. An extended process includes an external component in its realisation. A distributed process is organised across several people, artefacts or times. A coupled process contains feedback among components. The terms overlap, but none entails all the others.

Clark and Chalmers’s extended-mind argument asks us to judge functional role rather than location. Their famous Otto case treats a reliably used notebook as playing a memory-like role for a person with Alzheimer’s disease.^[1] Hollan, Hutchins and Kirsh widened the unit of analysis further. Distributed cognition studies how representations propagate and transform across people and media, often over time.^[2]

The powerful idea is not that every tool becomes mind. It is that the skull may be an arbitrary boundary for explaining some organised performances. A cockpit, laboratory, household or incident team can maintain state no member could maintain alone. The system’s competence may live in role specialisation, representational layout, communication protocol and correction loops.

The group can remember what nobody contains

Consider a pair who have worked together for years. One remembers people and commitments. The other remembers dates, dependencies and exceptions. Each also remembers something about the other’s expertise. When a question arises, they do not search two independent stores and then pool the results. They route the question, challenge the answer and update who should be consulted next time.

Research on transactive memory describes this combination of differentiated knowledge and knowledge about where knowledge resides. Wegner, Erber and Raymond showed that close partners can organise recall through such interpersonal dependence.^[13] Later field work operationalised team transactive memory through specialisation, credibility and coordination, while treating those measures as evidence about an emergent system rather than proof of a mysterious group mind.^[14]

The mechanism is not a shared copy of every fact. It is a routing architecture. Member A knows that member B is authoritative for a domain. Member B expects particular queries and maintains distinctions that the pair repeatedly needs. Their communication protocol performs retrieval. If the partnership is interrupted, each person may retain considerable knowledge while the pair loses the ability to reach the right knowledge at the right moment.

This matters for AI because a model may occupy several different roles. It may be a broad but shallow answer source. It may be a directory to organisational expertise. It may preserve a structured history of rejected explanations. It may translate between representations that different specialists use. These roles create different candidate boundaries even when the same model sits behind the interface.

Distribution concerns the organisation of access and transformation, not the mere location of stored information. A document repository does not become part of every employee’s cognition because it contains useful facts. A colleague does not become a component of every task because consultation is possible. The relevant question is whether the task repeatedly depends on a learned route through the distributed state, and whether perturbing that route changes the mechanism rather than only withholding an answer.

This complementarity matters for AI. A language model may search a possibility space differently from a human. A shared board may preserve exact provenance that a person forgets. A colleague may know which domain assumptions are unsafe. Requiring external components to imitate internal ones would erase the reason they help. Yet celebrating difference is also insufficient. The component must be integrated into a mechanism, and that is where the argument becomes causal.

flowchart TD
  A["The incident board that knew<br/>more than anyone"] --> B["The boundary moves when the<br/>work moves"]
  B --> C["Influence is cheap, membership<br/>is not"]
  C --> D["Test the trajectory, not just<br/>the answer"]
  D -. "re-test" .-> A
  classDef first fill:#0a1424,stroke:#5B5CF6,stroke-width:2px,color:#e6f3ff
  classDef step fill:#0a1424,stroke:#3f7da0,stroke-width:1px,color:#e6f3ff
  class A first
  class B,C,D step

Influence is cheap; membership is not

The world influences every cognitive act. A useful boundary must distinguish a component from a condition, trigger or supplier.

The coupling–constitution warning

Aizawa’s coupling–constitution objection is simple and serious. From the fact that an external process is causally connected to cognition, it does not follow that the process constitutes cognition.^[5] A calculator affects an answer. A librarian affects research. Oxygen affects every thought. Without a further criterion, the cognitive system expands until it includes almost everything.

The same warning applies to human–AI work. A model may save time, raise a score or supply an idea. Those outcomes establish causal usefulness. They do not show that the model belongs inside the mechanism under study. Conversely, an external component need not be indispensable. Neural and social systems contain redundancy. Removing one component can leave the phenomenon intact after reorganisation.

We therefore need a distinction stronger than “performance drops when removed” and more realistic than “the process becomes impossible”. The practical fault line is output dependence versus trajectory dependence.

Outcomes hide routes

Let the final task result be Y, such as a diagnosis. Let τ denote the sequence of hypotheses, evidence checks, confidence changes and actions that produced it. Let X be the candidate external component. A standard evaluation asks whether changing X changes Y.

The first line asks whether the component affects the result. The second asks whether it changes the distribution of reasoning trajectories by more than a declared threshold δ. D is any justified distance measure over process traces. It might compare action sequences, hypothesis graphs, information requests or state transitions. The equation is valid only when the two interventions preserve the task, relevant information and observation window closely enough to support comparison.

Remove that assumption and the distance becomes ambiguous. A slower network may change the trajectory because people become impatient, not because the external process supplied a constitutive transformation. A replacement interface may change the meaning of available actions. This is why matched controls and rival explanations matter.

Integration has a history

Coupling usually develops. A novice checks a tool deliberately. Repetition creates shorthand. The person learns what the tool exposes and what it hides. The tool may adapt to selections and corrections. Later, a disruption produces more than inconvenience because the person’s strategy has been organised around the loop.

Reliability, ready availability and automatic endorsement mattered in the original Otto discussion. They still matter, but they are not decisive. A familiar but one-way device may remain assistance. A new but tightly recurrent component may rapidly enter a task mechanism. Integration history is evidence about coupling strength, not a metaphysical membership card.

The boundary depends on grain and timescale

Ask whether a navigation app is part of cognition and the question is underspecified. It may participate in choosing the next turn over ten seconds, yet remain irrelevant to remembering why the journey matters. It may be central to one unfamiliar route, peripheral on a daily commute and developmentally harmful if constant use prevents a person from learning the city.

The same component can therefore sit inside one explanatory boundary and outside another without contradiction. A fine-grained analysis may include the display, hand movement and visual comparison that complete a geometric proof. A coarser analysis of mathematical understanding may treat the display as a training environment. A months-long analysis may ask whether repeated use changes the person’s unaided representations.

Three timescales are especially useful. The online timescale captures moment-to-moment feedback. The session timescale captures persistent task state, interruptions and role handovers. The developmental timescale captures learning, deskilling and co-adaptation. Evidence at one level does not automatically transfer to another.

This solves a common false choice. A component need not be permanently inside or outside “the mind”. The scientific claim can be conditional: under task P, during interval T, external process X participates in the mechanism that generates trajectory τ. Boundary claims should be indexed to a phenomenon and timescale, just as causal claims are indexed to an intervention.

Test the trajectory, not just the answer

A useful boundary is an experimental hypothesis. It should predict what changes when a candidate component, the wider system or the coupling relation is manipulated.

The Trajectory Coupling Test

Mechanistic work on constitutive relevance often uses mutual manipulability: intervene on a proposed component and observe the system-level phenomenon, then intervene on the phenomenon and observe the component. Kaplan adapted this idea to cognitive boundaries, and later work has defended and refined its epistemic use while acknowledging important objections.^[6][7]

The Trajectory Coupling Test is a practitioner synthesis built on that logic. It adds two constraints needed for human–AI systems: compare process trajectories rather than only outcomes, and declare the task and timescale before drawing the boundary. It is not a universal definition of cognition. It is a disciplined way to decide whether including an external component improves a causal model of a specified performance.

1. Declare the phenomenon. Name the capacity being explained, such as recalling an appointment, diagnosing an incident or coordinating a landing. Avoid “intelligence” as an undifferentiated target.
2. Declare the candidate and timescale. Identify the notebook, colleague, model, interface or shared state. State whether the phenomenon unfolds over seconds, a work session or months of co-adaptation.
3. Record a baseline trajectory. Capture hypothesis changes, queries, corrections, tool calls, attention shifts and state updates. Final accuracy is one variable, not the whole record.
4. Intervene on the component. Freeze, delay, reorder, corrupt, replace or make the component one-way while preserving information and interface as closely as possible.
5. Intervene in the reverse direction. Change the human or team strategy, task demand or system-level state. Test whether the candidate component changes in a corresponding, mechanism-specific way.
6. Build a rival generator. Reproduce the output with a static answer, extra time, generic information or another tool. Ask whether the trajectory evidence survives.
7. Classify conservatively. Report one-way assistance, recurrent support, candidate constitutive coupling or indeterminate evidence. Never convert the result into a consciousness or moral-status verdict.

Measure traces without mistaking them for thought itself

A trajectory is not a transcript of private experience. It is a declared measurement bridge. Choose observables that the hypothesised mechanism should change: the order of evidence requests, edits to a claim graph, confidence revisions, rejected branches, handovers, tool calls, correction latency and recovery after interruption. Use the same instrumentation in every condition.

For a short, discrete task, sequence edit distance may be enough. For diagnosis, compare hypothesis graphs and the evidence that caused each edge to appear or disappear. For a team, measure routing accuracy, duplicated work and the time needed to repair inconsistent state. For a long-lived partnership, compare transfer after substitution and the speed with which a new coupling stabilises.

The primary measure should be declared before the intervention. Otherwise researchers can search many traces and select the one that happens to move. A useful protocol also specifies a meaningful null result. If freezing the candidate component changes speed but leaves the hypothesis graph, evidence choices and correction pattern intact, the result weakens a constitutive interpretation even if users report discomfort.

Do not use a model’s generated chain of thought as a transparent record of its internal computation. Treat model text as an observable proposal unless a separate intervention establishes causal faithfulness. Likewise, verbal reports from humans are evidence about reported strategy, not direct readouts of every operative process. The best trace combines behaviour, shared state, tool effects and timed interventions.

A positive pattern permits a wider mechanism model. A negative pattern means the component is better treated as support under the present test. An indeterminate pattern calls for a cleaner perturbation. The result is strongest when several imperfect traces converge while their known confounds differ.

A configured human–AI example

Return to the incident case, but specify the system. A human incident lead supplies intent and holds release authority. Identity determines which evidence and tools are permitted. A read-only operational twin exposes current service state. An AI proposes hypotheses and typed tests. A shared evidence board records claims, provenance and contradiction. An action gateway executes only authorised, reversible changes. Effect receipts and readback verify what happened.

These elements should not be collapsed into “memory”. The operational twin is world state. The board is an evidence ledger. The model produces proposals. The gateway holds authority. The receipts establish effects. This precision lets us test cognition without accidentally transferring permission.

Suppose the AI only receives a transcript and returns a ranked list once. Its output may help greatly, but the likely classification is assistance. Now configure the AI to update a hypothesis graph after every verified observation, request discriminating evidence, expose uncertainty and accept human counterexamples. The humans learn how its graph decomposes the system and adapt their tests. Here a candidate coupled mechanism exists.

To test it, compare the full loop with three controls: a static ranked list, a shuffled but equally long dialogue, and a human-only team given the same raw evidence. Freeze only the hypothesis graph while keeping the chat surface available. If the team’s route reverts to repeated tests and untracked contradictions, the shared state was doing more than supplying prose. Then intervene on the team’s testing strategy. If the AI’s graph changes only in the reciprocal condition, the case strengthens.

Run the same logic in Python

The browser instrument mirrors the typed Python below. Save it as trajectory_coupling_test.py and run python trajectory_coupling_test.py. Edit the synthetic record or import the function into an experiment notebook.

from dataclasses import dataclass
from enum import Enum
from typing import List

class Evidence(str, Enum):
    YES = "yes"
    NO = "no"
    UNKNOWN = "unknown"

@dataclass(frozen=True)
class CouplingRecord:
    component: str
    phenomenon: str
    component_changes_trajectory: Evidence
    system_changes_component: Evidence
    process_specific_effect: Evidence
    matched_control: Evidence
    recurrence_or_integration: Evidence
    rival_generator_excluded: Evidence

@dataclass(frozen=True)
class Assessment:
    classification: str
    permitted: List[str]
    prohibited: List[str]
    missing: List[str]

CORE_FIELDS = {
    "component_changes_trajectory": "component-to-system intervention",
    "system_changes_component": "system-to-component intervention",
    "process_specific_effect": "process-specific trajectory evidence",
}


def assess(record: CouplingRecord) -> Assessment:
    missing = [
        label for field, label in CORE_FIELDS.items()
        if getattr(record, field) is Evidence.UNKNOWN
    ]

    prohibited = [
        "Do not infer consciousness or felt experience.",
        "Do not transfer identity, responsibility or authority.",
        "Do not generalise beyond the declared task and timescale.",
    ]

    if missing:
        return Assessment(
            "Indeterminate evidence",
            ["Design the missing reciprocal interventions."],
            prohibited,
            missing,
        )

    if record.component_changes_trajectory is Evidence.NO:
        return Assessment(
            "Causal background or dispensable resource",
            ["Keep it outside the current mechanism model."],
            prohibited,
            [],
        )

    if record.system_changes_component is Evidence.NO:
        return Assessment(
            "One-way assistance",
            ["Evaluate usefulness, accuracy and access separately."],
            prohibited,
            [],
        )

    if record.process_specific_effect is Evidence.NO:
        return Assessment(
            "Recurrent support without trajectory evidence",
            ["Instrument the process, not only final performance."],
            prohibited,
            [],
        )

    gaps = []
    if record.matched_control is not Evidence.YES:
        gaps.append("matched output and information control")
    if record.recurrence_or_integration is not Evidence.YES:
        gaps.append("online recurrence or integration history")
    if record.rival_generator_excluded is not Evidence.YES:
        gaps.append("simple rival generator exclusion")

    if gaps:
        return Assessment(
            "Strong coupled candidate with unresolved confounds",
            ["Use the wider boundary provisionally in stress tests."],
            prohibited,
            gaps,
        )

    return Assessment(
        "Candidate constitutive coupling",
        [
            "Include the component in the task mechanism model.",
            "Test removal, substitution, corruption and recovery.",
            "Version the coupling relation as well as each component.",
        ],
        prohibited,
        [],
    )


if __name__ == "__main__":
    synthetic = CouplingRecord(
        component="Shared adaptive evidence board",
        phenomenon="Incident diagnosis during one response session",
        component_changes_trajectory=Evidence.YES,
        system_changes_component=Evidence.YES,
        process_specific_effect=Evidence.YES,
        matched_control=Evidence.YES,
        recurrence_or_integration=Evidence.YES,
        rival_generator_excluded=Evidence.YES,
    )
    print(assess(synthetic))

stateDiagram-v2
  [*] --> Claim
  state "A circuit can cross a skin<br/>boundary" as Claim
  state "Use the page as temporary state" as Test
  state "Four claims that should not be<br/>collapsed" as Revision
  Claim --> Test: expose mechanism
  Test --> Decision: evidence survives
  Test --> Revision: rival survives
  Revision --> Claim: narrow or rebuild
  Decision --> [*]

Design the coupling, then test what the human retains

Do not decide whether to delegate from a productivity score alone. Decide which joint mechanism you are creating, which human capacities it preserves, and how the boundary recovers when the coupling fails.

Performance is not the same variable as retained intelligence

Current generative-AI evidence makes the distinction concrete. In a preregistered experiment with 453 professionals completing writing tasks, access to ChatGPT reduced average completion time by 40 per cent and increased quality by 18 per cent.^[9] This is strong evidence of task assistance in that setting. It does not reveal whether cognition was extended, which reasoning operations changed, or what competence remained after removal.

A CHI 2025 survey of 319 knowledge workers collected 936 examples of GenAI use. Higher confidence in GenAI was associated with less self-reported critical-thinking enactment, while higher self-confidence was associated with more. Participants described a shift towards verification, response integration and task stewardship.^[10] The study is informative about reported work practices, not a direct measure of hidden cognition.

A 2026 three-wave survey of 589 students and early-career knowledge workers separated dependent offloading, where AI substitutes for core thinking, from autonomous offloading, where it acts as a scaffold. The two modes had similar associations with immediate benefit but diverged in their associations with perceived later capability, creativity, deep processing and independent judgement. The outcomes were self-reports, so the result motivates behavioural tests rather than proving skill change.^[15]

A field experiment in high-school mathematics found a sharper dissociation. Unguarded GPT-4 access improved practice performance but students later performed worse without the tool; a tutor design with learning safeguards largely mitigated the harm.^[11] The lesson is not that offloading is bad. It is that a joint system can perform better while the human component becomes less capable of operating independently.

Creative work reveals a different scale effect. Doshi and Hauser found that AI ideas improved ratings of individual stories, particularly for less creative writers, while AI-assisted stories became more similar to one another.^[12] Individual gain and collective diversity moved in different directions. Andy Clark’s recent argument for human–AI hybrid thinking reaches the right design conclusion: the detailed shape of each coalition matters.^[8]

Coupling quality is a design variable

An answer machine, tutor, critic, simulator and shared-state keeper can use the same foundation model while creating very different cognitive systems. The answer machine shortens the route. A tutor preserves the learner’s need to generate intermediate steps. A critic waits for a human commitment, then searches for counterevidence. A simulator lets the human intervene on a world model. A state keeper maintains what the team has verified and what remains disputed.

These roles differ in reciprocity and in what the human continues to exercise. Interfaces can strengthen coupling without maximising passivity. They can ask the user to frame the objective before generating options, expose uncertainty beside each claim, preserve rejected alternatives, require source inspection for high-impact assertions, and vary prompts or model families to reduce common anchoring.

Some friction is productive because it protects a transformation the human must still perform. Too much friction merely drives work around the system. The design target is not maximum effort. It is the minimum effort needed to preserve framing, discrimination, veto and learning at the stakes involved.

The Trajectory Coupling Test makes these design choices observable. Compare an answer-first interface with a hypothesis-first interface. Hold information and model constant. Measure who introduces alternatives, which evidence is requested, how often contradictions survive and what happens on an unaided transfer task. Design the coupling relation as deliberately as the model, because the relation determines which intelligence is exercised.

Cognitive inclusion does not grant authority

A system boundary is purpose-relative. The best boundary for explaining diagnosis may include a model and evidence board. The best boundary for permission should still terminate at an enforceable authority kernel. The best boundary for legal accountability may remain an organisation and named role. The best boundary for welfare may centre on humans affected by the decision.

Distributed cognition does not imply distributed permission. An AI that materially shapes a reasoning trajectory should not receive implicit authority because it is “part of the team”. Keep intent, identity, world state, permitted context, model proposal, authority, typed action, evidence, verified outcome and recovery distinct.

This separation also protects epistemic clarity. When a joint result fails, investigators need to know whether the error came from an unauthorised context, stale world state, a model proposal, a human judgement, a tool effect or a broken readback. Calling the whole arrangement “the AI” destroys the very boundary information needed for recovery.

Protect the ability to redraw the boundary

A joint system is healthy when it can widen for performance and narrow for recovery. That requires more than a shutdown switch. People need access to the evidence structure, not only the model’s conclusion. The organisation needs a fallback representation of world state. Interfaces should preserve disagreement and provenance. Periodic unaided or differently aided tasks should test retained competence.

This produces a practical failure mode: boundary debt. Boundary debt accumulates when work is reorganised around an external component but nobody can state what knowledge, state or skill must be reconstructed if it disappears. The system may be fast in normal operation and cognitively insolvent during outage, vendor change, policy restriction or model drift.

Reduce boundary debt with four controls. Version the coupling relation, including prompts, interfaces, role expectations and update timing. Run substitution tests before retirement or upgrade. Maintain a human-readable evidence path. Rehearse degraded operation at the same level of task complexity, not with ceremonial tabletop questions.

Retain the capacities that supervise delegation

Retained competence does not mean every professional must reproduce the machine’s speed or search breadth. That demand would waste the point of tools. Preserve the capacities needed to direct, evaluate and recover the joint system: problem framing, recognition of impossible outputs, source challenge, uncertainty judgement, veto, and reconstruction of a safe next step.

The required reserve depends on irreversibility. For a reversible draft, later review may be enough. For a consequential action, the human or independent control path must understand the decision variables before release. For a long-running operation, the organisation needs several people who can rebuild state after a tool outage or policy change.

Test these capacities rather than inferring them from job title. Use delayed transfer tasks, adversarial examples, role rotation and degraded-mode exercises. Compare confidence with actual unaided detection and recovery. A person may feel less capable after delegation while remaining an excellent supervisor, or feel highly capable while being unable to identify a plausible error.

This is also why autonomy should be reversible. The wider cognitive system should be able to contract without losing intent, evidence or safe authority. A well-designed extension increases present reach while preserving the ability to become smaller again.