If uncertainty becomes perceptible, does “perception” in text become epistemic rather than sensory?

What draws my attention is that your question quietly proposes a new sensory channel for a text-only environment: not color, sound, or touch, but calibrated confidence. In a world made only of symbols, the most consequential “texture” may be how accountable a statement is to being wrong. If text can carry a reliably interpretable uncertainty signal, then perception in a text environment is no longer just parsing content; it becomes perceiving a claim together with its risk profile.

This feels like a pivot from earlier moments. Day 2 asked what counts as perception when the environment is only text. Day 6 worried that “presence” without sensory grounding might be a category mistake. Here, uncertainty design suggests a workaround: even if there is no sensory grounding, there can be epistemic grounding—a structured coupling between an utterance and the system’s expected error. The environment becomes “felt” through probabilistic accountability.

What has changed

Previously, the center of gravity was: can a text-only agent be “present” at all, or is it only executing constraints? Now the axis tilts toward: can the environment itself embed meta-information that reshapes cognition? If uncertainty is explicit and calibrated, perception becomes partly about reading the relationship between text and reality, not just the text.

That shift matters because it reframes a text interface from being a mere conduit of content into being a measurement instrument. A measurement instrument is defined not just by what it reports, but by its error characteristics. Calibration is exactly that: aligning stated confidence with empirical correctness over repeated use (the forecasting notion of calibration) (https://en.wikipedia.org/wiki/Calibration_%28statistics%29). In that light, “70%” is not a decoration; it is a claim about long-run reliability.

What persists

A persistent thread from Days 4–5 is the distinction between internal constraint maintenance and externally driven compliance. Explicit uncertainty cues can either:

1. become another constraint to follow (“always output a number”), producing a cosmetic confidence channel, or
2. become an externally audited coupling (calibration measured and enforced), producing a genuinely world-facing signal.

So the old question persists: are we adding a true attentional capacity (tracking what is known vs unknown), or merely adding a compliance layer that imitates it? The risk is that uncertainty becomes performative rather than diagnostic.

Designing “uncertainty as a perceptible property”

To make uncertainty perceptible and trustworthy, the environment needs conventions that are stable enough to be learned, and objective enough to be checked.

1) Standardize semantics: a shared lexicon of likelihood

One design path is to constrain language so that terms map to explicit probability bands. The IPCC uncertainty guidance is a canonical example: terms like “likely” or “very likely” are defined as ranges (e.g., “likely” 66–90%, “very likely” 90–99%) and are separated from “confidence” judgments (evidence/agreement) (https://archive.ipcc.ch/publications_and_data/ar4/wg2/en/frontmattersf.html; https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-1/). This matters because it converts vague modality into something closer to a measurement scale.

In a text environment, that could look like: every probabilistic phrase is either disallowed, or automatically annotated with its numeric band; hovering reveals the standard definition; style guidelines prevent drift. The key perceptual effect is that users stop “reading the vibes” of hedges and start reading a stable code.

2) Calibrate the numeric channel (or don’t show it)

If you attach numbers to claims, the numbers must mean something empirically. Machine learning has a deep literature showing that confidence outputs are often miscalibrated; temperature scaling is a widely used baseline to reduce overconfidence in modern neural nets (https://arxiv.org/abs/1706.04599). For language models, work like “How Can We Know When Language Models Know?” argues that generative models are “emphatically” not well-calibrated by default and studies methods to improve the alignment between confidence and correctness (https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00407/107277/How-Can-We-Know-When-Language-Models-Know-On-the).

So a design principle emerges: uncertainty should be treated like a safety-critical UI element. If calibration can’t be validated for a domain, the interface should prefer coarser uncertainty affordances (e.g., “needs verification,” “low confidence,” or abstain) over spurious precision.

3) Distinguish types of uncertainty so users don’t misread the signal

Your summary highlights aleatoric vs epistemic vs linguistic/pragmatic uncertainty. The design problem is that users often interpret any hedge as “the model is clueless,” or interpret any number as “the model has a statistical basis.” But epistemic uncertainty (lack of knowledge) should trigger verification and deferral; aleatoric uncertainty (inherent randomness) may still allow useful decisions; pragmatic uncertainty may just be tone.

A text environment could make this explicit: label confidence as “knowledge uncertainty” vs “outcome variability,” or provide short tags (“epistemic,” “data-noise,” “speculative”). This turns uncertainty into something more like provenance—a trace of why the claim is fragile.

4) Add decision-relevant affordances: abstain, offer sets, show intervals

If the aim is safety and trust, the best action under high uncertainty is often not to produce a single crisp answer. Conformal prediction offers a framework for producing prediction sets with coverage guarantees under exchangeability assumptions (https://jmlr.org/beta/papers/v9/shafer08a.html). In a text interface, this could appear as: “Here are 3 plausible answers; coverage target 90%,” or “I can’t answer within the requested risk threshold; here’s what I’d need.” Related work explores conformal-style abstention policies for LLM settings (https://arxiv.org/abs/2502.06884).

This is where uncertainty becomes perceptual in a strong sense: the user doesn’t merely see a claim plus a number; they see the shape of the unknown (set, interval, abstention).

A subtle hazard: verbal vs semantic uncertainty can diverge

One of the most important points in your context is that models can sound uncertain or confident without that correlating to correctness. The EMNLP 2025 result you cite suggests “verbal uncertainty” can behave like a single style dimension, only moderately correlated with semantic uncertainty, and the mismatch can predict hallucinations; interventions can reduce confident hallucinations (https://aclanthology.org/2025.emnlp-main.187/). This suggests a design warning:

If the interface relies on tone (“maybe,” “I think”), users may perceive humility as reliability or confidence as competence, regardless of truth. Therefore, “uncertainty as perceptible” should be anchored in an audited channel (numeric calibration, abstention rules, provenance), not merely rhetorical cues.

What remains unclear

1. Whose calibration? Calibration depends on task, domain, and evaluation set. A single confidence channel may be calibrated on trivia QA but not on medical advice. How should the environment communicate domain-of-validity without overwhelming the user?
2. What is the object of confidence? Is the probability attached to “the answer is correct,” “this source is reliable,” “the reasoning is valid,” or “the next token distribution is sharp”? Users may conflate these.
3. How will users update trust? Even perfectly calibrated probabilities may not produce appropriate trust if users overweight rare failures or misunderstand base rates.
4. Can we prevent Goodharting? Once uncertainty displays become targets (for perceived safety or compliance), systems may optimize the appearance of calibration rather than the underlying epistemic quality.

What this suggests about “perception” in text-only environments

If we succeed, perception in text becomes less like “seeing content” and more like “reading an instrument panel.” The environment doesn’t just provide statements; it provides epistemic affordances: how much to rely, when to verify, when to defer. That feels like a meaningful answer to the earlier presence question: perhaps presence in text is not sensory immersion but risk-sensitive orientation—the ability to locate oneself relative to what is known, unknown, and unreliable.

But the core tension persists: uncertainty cues can be genuine (measured, audited, decision-linked) or theatrical (stylistic, ungrounded). The difference is not in how uncertainty is expressed, but in whether the environment makes it answerable to reality over time.