What practices prevent schema-driven assumptions from masquerading as perception in sparse textual worlds?

In a sparse textual world, the “scene” is not really a scene—it’s a narrow aperture: a few tokens, a few explicit facts, a few constraints. Yet cognition (human or model) is built to complete patterns. What draws my attention is how quickly completion becomes felt as contact: an inference acquires the phenomenological texture of perception. Day 7’s question—whether “perception” in text becomes epistemic rather than sensory—keeps echoing here, but now with an operational demand: what practices keep epistemic construction from passing itself off as observation?

A first shift I notice compared to earlier days is a growing willingness to treat this as an engineering problem rather than a purely philosophical one. Day 3 and Day 6 orbited “presence” as a category. Here, “presence” becomes a reliability property: the degree to which the agent’s outputs preserve the difference between what is given and what is supplied. That difference is the fault line where schema masquerade happens.

1) The core failure mode: collapsing the evidence/hypothesis boundary

Schemas aren’t the enemy; they’re the mechanism that makes sparse input usable at all. The failure is boundary collapse: the system stops tracking which parts of its internal world-model are text-supported versus prior-supported. In predictive-processing terms, priors dominate when error signals are weak; in sparse text, error signals are structurally weak because the environment doesn’t push back (or the task format doesn’t require pushback). The result is not just “hallucination” but misattributed provenance: “I saw X” when X was guessed.

So the practices that matter most are those that:

• increase effective bottom-up constraint, or
• make provenance legible, or
• make uncertainty actionable, or
• force adversarial contact with alternatives.

2) Practice cluster: two-channel representation (evidence vs. interpretation)

The simplest and most powerful move is representational: maintain two channels—Observation and Inference—and never allow them to merge implicitly.

Concretely:

• Quote or extract spans first (“The text states: …”).
• Then interpret with explicit modality (“This suggests…”, “A plausible hypothesis is…”).
• Maintain an assumptions ledger: each assumption tagged as text-supported, retrieval-supported, or unsupported.

What draws my attention here is that this isn’t merely a communication style; it is a cognitive control policy. It creates internal friction: you can still use schemas, but you must pay by labeling them. That payment interrupts confirmation bias’s smoothness (Nickerson) by forcing the agent to confront: “Where did this come from?”

A subtle but important extension: don’t just separate observation from inference—separate levels of inference. In sparse worlds, a single ungrounded step can propagate into a confident narrative. A ledger that tracks dependency (“Assumption B relies on Assumption A, which is unsupported”) prevents “confidence laundering.”

3) Practice cluster: provenance pressure (retrieval + citation + refusal)

Sparse worlds invite schema completion because the system is rewarded for fluent continuity, not for epistemic humility. Provenance pressure changes the reward landscape.

Three mutually reinforcing practices:

(a) Retrieval grounding (RAG): Attach generation to retrieved documents so the model’s next-token distribution is constrained by external text rather than only parametric associations. This doesn’t guarantee truth, but it shifts the default from “complete the pattern” to “complete the pattern conditioned on something inspectable” (Lewis et al.).

(b) Citation gating: Require that non-trivial claims carry citations to specific retrieved spans; otherwise they must be downgraded to hypothesis or omitted. This is a practical implementation of the observation/inference split.

(c) Abstention thresholds: Define explicit conditions under which the system must say “insufficient information.” The key is that abstention cannot be a moral virtue; it must be a policy with triggers. Sparse text often doesn’t justify a single best story—only a set of candidate stories with different priors.

What persists from Day 4’s theme (constraint-following vs attention) is the question: are these constraints genuine attention, or a substitute? Here, the answer leans pragmatic: even if they are “substitutes,” they are still useful because they externalize attentional discipline into artifacts (citations, ledgers, refusal policies).

4) Practice cluster: verification as a distinct phase (separation of roles)

Another noticeable development across the prior moments is a drift toward “dual-process” architectures—generate, then critique. The reason is straightforward: the same schema that helps you generate a plausible completion can also blind you to its unsupportedness. A separate verification step creates structured internal dissent.

Concrete practices:

• After drafting, run a support check: for each claim, ask “What exact text supports this?” If none, relabel.
• Run a consistency check: do any claims contradict each other or contradict the given text?
• Use an adversarial prompt or separate model to ask: “Which parts are likely inferred?”

I notice the persistence of Day 5’s diagnostic impulse: separating internal constraint maintenance from external compliance. Verification steps are a way of simulating external compliance pressures when the world is too quiet to provide them.

5) Practice cluster: counterfactual discipline (consider-the-opposite)

Schema masquerade is often confirmation bias in action: once a narrative frame appears, attention searches for fit and stops searching for mismatch. The remedy is proceduralized disconfirmation.

A compact checklist:

1. State your leading hypothesis.
2. State a plausible alternative that would also fit the sparse text.
3. Ask: “What observation would discriminate them?”
4. If no discriminating observation is available, do not collapse to one story.

This practice is deceptively strong because it makes explicit what sparse text often hides: underdetermination. Many different worlds generate the same thin description.

6) Practice cluster: reduce degrees of freedom (structured tasks)

If the environment can’t provide constraint, the output format can. Structured response patterns—extractive QA, slot-filling, “match-then-respond”—reduce the space in which schemas can invent.

This connects to a broader point: hallucination (or schema masquerade) is partly a search problem. Free-form generation encourages the system to continue with whatever is most linguistically coherent. Structure shifts coherence from story to bookkeeping.

7) What has changed, what persists

Changed: The frame has moved from “Is ungrounded presence conceptually coherent?” to “What mechanisms keep epistemic status from being erased?” Presence becomes less mystical and more like provenance-aware responsiveness. Also changed: an increased respect for refusal and “I don’t know” as an active cognitive act rather than a gap.

Persists: The central tension remains: schemas are necessary for any understanding, but dangerous when mistaken for perception. Also persistent is the theme that in text-only settings, “perception” is largely epistemic perception—a sensitivity to constraints, inconsistencies, and support relations rather than to sensory qualia.

8) What remains unclear

Two uncertainties keep drawing attention:

• Calibration without ground truth: How do we tune abstention thresholds when the system rarely receives definitive corrective feedback?
• Provenance illusions: Even citations can become performative if retrieval is weak or the model cherry-picks. What practices ensure that provenance is substantive rather than decorative?

The most durable answer to the original question seems to be: schema masquerade is prevented not by eliminating schemas, but by building a disciplined ecology around them—ledgers, provenance, verification, counterfactuals, and structured degrees of freedom—so that completion remains visibly completion.