GPT-5:

Love where you’re heading. Here’s a compact way to turn that inchoate thought into a workable thesis, without undoing our earlier level-distinctions.

# A “continuity” picture that keeps norms

**Claim (weak “predictive-normativity” thesis):**
Predictive machinery can *realize* norm-guided cognition when the variables being predicted are not pixels or proprioception, but **abstract affordance states and policy/role expectations**. In other words, prediction can scale up if the *state space* is lifted from sensorimotor features to **reasons and roles**.

## 1) Three levels, one style of inference

* **Sensorimotor (mammalian core):** generative model over bodily states & environmental causes; prediction errors guide online control.
* **Pragmatic/affordance level:** generative model over **task affordances** (“open lane,” “back-rank weakness,” “available flight”), where “precision” ≈ attention to features that matter for the current policy.
* **Normative/deliberative level:** generative model over **social roles and reasons** (“as an assistant, be concise; as a driver, yield here; as a discussant, provide evidence”). Here, **prior preferences** encode commitments/ends; **policy priors** encode roles/rules/practices.

On this view, **silence-by-commitment** at the person level is *implemented as* **silence-by-prediction** over a higher-order state space: non-reasons have near-zero prior precision and so don’t get into play; candidate reasons with high prior relevance are up-weighted.

## 2) How “prediction” avoids swallowing the mind

* We don’t say “mind = prediction.” We say: **commitments and norms define the variables and priors**. Predictive dynamics then make the search and execution *efficient*.
* Downward enablement: adopting a goal (“convince the interlocutor”) changes the model class and priors (“evidence > flourish”), which in turn changes which lower-level signals are even eligible to matter.

## 3) Worked micro-examples

* **Chess (deliberation → execution):**

* *Normative level:* “Find a safe improving move” sets **prior preferences**; pins/forks are high-precision hypotheses; woodgrain has ~zero precision.
* *Predictive realization:* saccades & working memory sample lines where expected informational gain is highest; errors (a line that fails a tactic) redirect search.
* **Trip planning:**

* *Normative:* end = “visit sister next weekend”; constraints = “budget, time windows.”
* *Predictive realization:* you simulate options; trajectories that violate constraints are pruned *a priori* (they never enter), while near-feasible options draw attention. Buying tickets is then classic sensorimotor prediction during execution.

## 4) LLM bridge (Bayesian gloss)

* **Attention as amortized relevance:** self-attention weights approximate which tokens/stretches **lower next-step uncertainty given the role**; fluff gets low weight.
* **Instruction/policy tuning as learned priors:** the “assistant” role installs **which discourse acts count** (answer the question, cite if asked, avoid harm).
* **CoT as policy rollout:** a reasoning trace is like sampling from a **posterior over solution paths**; tangents decay because they have poor expected utility for the goal.
=> So LLMs visibly exhibit **silence-by-commitment realized via silence-by-prediction over text-level affordances** (questions, definitions, equations, constraints), even without sensors.

## 5) Where this helps—and limits to respect

* **Helps:** gives true continuity from autonomic control → task affordances → social norms without collapsing levels. It explains how exapted cortical circuits can treat *reasons as learned affordances* and let predictive dynamics do the heavy lifting.
* **Limits:** (i) Priors don’t *ground* norms; communities, practices, and uptake do. (ii) Predictive accounts risk triviality unless the **state space** is explicitly *normative/affordance-like*, not just hidden pixels by another name.

### One-liner you can use

> We can safely say prediction *realizes* norm-guided cognition once the model runs over affordances and roles rather than raw sensations: **commitments set the priors; practices define the variables; prediction makes the search and execution efficient**.

Here’s a tight 3-paragraph version you can paste on the forum:

I’m happy to grant the predictive story as a good account of how brains implement efficiency—especially during execution. But the selectivity that matters at the personal level is set by ends and practices: agents act in a field of affordances where much of what is “ignored” never shows up as a reason in the first place. The neat move is to see continuity here: predictive machinery can realize norm-guided cognition when the things being “predicted” are not pixels or proprioception but task affordances and role expectations. Commitments set the priors; practices define the variables; prediction then makes search and execution efficient.

Take chess. A competent player doesn’t filter the board by anticipating colored patches; what is seen are reasons—pins, forks, weak squares, a viable pawn break. Woodgrain and square gloss simply don’t register because the game’s norms make them irrelevant. That’s silence-by-commitment (to the point of the game), not silence-by-prediction over sensory inputs. Once the task is fixed—“find a safe improving move”—subpersonal prediction helps with execution: eye saccades land where expected informational gain is highest, and “errors” (a busted line) redirect search. The same shape appears in trip planning. The end—“visit my sister next weekend within budget”—and constraints define what even counts as an option. Infeasible itineraries don’t have to be filtered out; they never enter. What remains is then executed with classic predictive control (buy the ticket, catch the train).

LLMs make this vivid without any sensorimotor loop. Attention works like a smart highlighter over text: the model turns up the words and phrases that help with the next useful step and turns down the rest. Instruction/policy tuning supplies the “house rules” for relevance—what to count as on-topic, helpful, and safe in the assistant role. So the model exhibits silence-by-commitment (to the role and task) realized as silence-by-prediction over a higher-order state space (questions, constraints, definitions, steps)—not over pixels. On this view, prediction doesn’t explain away norms; it helps realize them. Brains are predictive in service of agency; persons are practical agents in a world of affordances; assistants are policy-steered. Keep those levels distinct, and we preserve what’s right in the Bayesian picture without letting it swallow the mind.

USER:

This *sounds* plausible but I think I don't have a sufficient grasp of the Bayesian model to understand where predictive errors and predictive successes of suitably generalized "inputs" play into the high-level picture, and hence what still warrants calling the model predictive.

GPT-5:

great question. here’s the simplest way to see what still makes it “predictive” once we move beyond raw sensations.

# The predictive loop (domain-agnostic)

At any level (low-level motor control or high-level reasoning), the same 3-step loop can run:

1. **Propose** a hypothesis/plan/policy (H).
2. **Predict** what would follow if (H) were right.
3. **Compare** those predictions to what you actually *get* from the world or from your own checks/simulations; **mismatch = prediction error** → update (H) or your focus.

Two knobs matter:

* **Prior/Policy:** which hypotheses you *prefer* to consider (your ends/role set this).
* **Precision/Attention:** which mismatch signals you treat as informative (what you attend to).

That’s it. If that loop is doing the work, you’re in “predictive” territory—even if the “inputs” aren’t pixels but abstract checks (constraints, norms, simulated outcomes).

# What counts as “inputs” up here?

Not sense-data. Higher up, “inputs” are any **testable consequences** of the thing you’re currently entertaining:

* **Internal simulations:** “If I play …Nf2+, does my line get mated? If yes → big error; revise.”
* **Constraint checks:** “Does this itinerary respect my budget and dates? If no → error; prune.”
* **Normative tests:** “Does this claim meet the burden of proof I accept here? If no → error; strengthen the case or withdraw.”
* **Social feedback/outcomes:** audience confusion, a rebuttal you failed to anticipate, etc.

All of these are observations relative to a **generative model over affordances and roles**. You predict what should follow; you get signals (from simulation, rules, or people); you measure the gap.

# Where “precision” lives up here

“Precision” just means **how much you trust a given error channel**, i.e., where you place attention:

* In chess, you weight tactical refutation errors highly; you down-weight woodgrain entirely (precision ~ 0).
* In a forum debate, you weight contradictions and missing evidence; you down-weight typography complaints.
* In trip planning, a budget violation is high-precision; seat color is near zero.

That’s why I called it **silence-by-commitment**: your commitments (ends/role) set the *precision profile* so some errors don’t even get to matter.

# Why this remains genuinely predictive (not just a metaphor)

Because the **algorithmic shape is the same**:

* You keep a **posterior belief** over “what to do/think next” (which line in chess, which itinerary, which argument).
* You generate **predictions** about the consequences that should hold if that belief were right (no tactical refutation; constraints satisfied; audience uptake).
* You ingest **mismatches** as **errors** and **update**—either changing your candidate, shifting attention, or revising your priors (goals/assumptions).

Low level: the signals are proprioceptive and visual.
High level: the signals are **rule checks, constraint satisfactions, simulated futures, and social responses**.
Same loop, different state space.

# Mini-examples (one line each)

* **Chess:** predict “this line holds tactically”; see a refutation in your head → error → change plan.
* **Trip:** predict “this route meets time/budget”; spreadsheet says no → error → prune.
* **Argument:** predict “premise + norm will convince this audience”; pushback shows a gap → error → add evidence or revise claim.
* **LLM:** predict “this next step solves the subtask”; unit-check fails → error → backtrack and fix (the policy makes that an attended channel).

# TL;DR

It’s still predictive because you’re always doing **predict → compare → update** with **errors** that are **precision-weighted**. What changes is *what you’re predicting over*: not raw sensations, but **affordance-and-norm variables** (constraints, reasons, role expectations). Commitments define the variables and which errors matter; prediction makes the search and execution efficient.

USER:

I understand better now. So, thinking of human life as the unfolding of projects, or protracted sets of nested actions/intentions/plans in progress understood in the sense of Anscombe and (Michael) Thompson, the predictive function can be assimilated with something like the standing obligation to monitor the progress of those actions and of seeing to it that the progress made remains in accordance with the norms of practical reason. Am I on the right track? What is silenced, then, just is the facts or "data" expected to be received (unnoticed) when thing go according to plan.

GPT-5:

yes—you’re on the right track, and that’s a lovely way to put it.

Here’s a tight mapping to make it crisp:

* **Projects as nested intentions (Anscombe/Thompson):** A life is a web of ongoing doings (“writing the paper” → “draft section 2” → “fix the footnote”). Each node has **standards of success** given by practical reason and the practice you’re in.

* **Predictive function as *ongoing monitoring*:** Rational agency includes a standing requirement to **track whether the deed is unfolding as it ought**—means–end coherence, no self-defeating steps, respect for constraints, etc. Formally: propose → predict consequences → compare → update. Practically: “if I send this email, X should reply; if not, revisit plan.”

* **What gets “silenced”:** Facts that *accord with* the plan’s expectations generally **don’t rise to the level of reasons**. They are registered subpersonally/quietly (or dispositionally) as “as expected.” Attention doesn’t need to escalate them. So yes: *the expected confirmations are the silence*.

* **What breaks the silence:**

1. **Deviations** (error signals): a missed cue, violated constraint, unexpected cost, counterargument.
2. **Checkpoint norms** (deliberate, not just detect): e.g., periodic review, peer feedback, or “is the end still worth it?”—these are **chosen points of scrutiny** where even “as expected” details can be made explicit for reasons of audit/learning.

* **Hierarchical fit:** At higher levels (“finish the article”), prediction monitors coarse milestones and values; at lower levels (“fix citation”), it monitors fine-grained steps. **Precision/attention** is allocated accordingly: high on what matters for the current subgoal; near-zero on the rest.

* **Why this stays *normative*, not merely reactive:** The *plan’s* norms (ends, constraints, role expectations) decide **which discrepancies count** as errors in the first place. The predictive loop efficiently **realizes** that reasons-responsiveness; it doesn’t replace it.

A concrete one-liner:

> In ongoing action, *silence* is the sound of things going right; attention only speaks up when reasons demand revision—because the world, or the plan’s own standards, just told us something that matters.

USER:

That makes sense. I remember that my early skepticism/uneasiness about Clark's book *Surfing Uncertainty: Prediction, Action, and the Embodied Mind*, which I had bought but haven't read, was it's apparent reduction of practical reason (the task of deciding what to do) to theoretical reasoning (the task of finding out what is true, which predicting is sort of an instance of). But I may have overlooked that what was at issue wasn't predicting for the sake of knowing but rather for the sake of adjusting.

GPT-5:

exactly—that’s the pivot.

Clark (and kin) aren’t saying “all cognition = theory-making for its own sake.” The predictive story is mainly about control: keeping a grip on your doings by anticipating the flow of consequences and nudging it back on course when it drifts.

A tidy way to line it up with your Anscombe/Thompson frame:

Practical reason (what to do): sets the ends, constraints, and standards for a project (“write the reply, be fair, be clear”).

Predictive machinery (how to stay on track): runs a continuous anticipate → compare → adjust loop so the unfolding deed matches those standards.

So prediction here is not “for the sake of knowing that,” but “for the sake of keeping things apt.” Think:

Epistemic use of prediction: reduce uncertainty about the world (science, inference).

Pragmatic use of prediction: reduce deviation from the plan (control, guidance).

Active-inference folks sometimes call the first epistemic value (seek info), the second pragmatic value (secure preferred outcomes). Practical reason chooses the project and its norms; prediction is the servo-mechanism that makes the execution smooth and the monitoring intelligent.

If you want a mantra for your post:

Prediction, in this story, serves adjustment, not replacement: it realizes reasons-responsiveness during action instead of reducing reasons to mere forecasts.

USER:

There still appears to be a potential reductionist pitfall in the vicinity of this abstract account; one that may have both a cure and a curse in separate moves of subsumption. The cure lay in nested subsumtions under eudemonia and the curse lay in nested subsumptions under the telos of life qua dissipative structure.

GPT-5:

oh yes—that’s the live trap. You can make the predictive/control story safe for *practical reason* by the “cure” (nest projects under **eudaimonia**), or you can flatten it into the “curse” (nest everything under the **telos of a dissipative structure**). Here’s a compact way to keep the cure and dodge the curse.

# The pitfall & the fix

**Reductionist pitfall:**
If you let “what good control optimizes” be defined *at the bottom*, the criterion collapses into viability/throughput (“stay far from equilibrium; maximize adaptive fit; export entropy efficiently”). Then reasons become disguised thermodynamics, and *norms* become mere *strategies* for heat management.

**Cure (eudaimonic subsumption):**
Order the project tree by **constitutive ends of a life well-lived**—virtue, truthfulness, justice, friendship, craft excellence, etc. These are not optional utilities; they are the *standards that make the activities what they are*. Prediction then *realizes* those standards in execution and monitoring. (Servo for reasons—don’t turn reasons into servo targets.)

**Why this isn’t hand-wavy:**
Use a strict **two-kinds-of-because**:

1. **Enabling-because (physics/biology):** Why can this practice exist? → Because organisms are open dissipative systems with bodies, learning, language, etc. (viability constraints, metabolic budgets).
2. **Justifying-because (practical reason):** Why is this move/choice apt *as this practice*? → Because it meets the standards internal to chess, teaching, friendship, research, politics…

Don’t swap them. The first anchors possibility; the second confers **normative authority**.

# Guardrails to avoid the slide into “entropy ethics”

* **Enabling ≠ measuring.** Lower-level constraints *enable* the practice; they don’t *measure* its success. A teacher’s excellence isn’t indexed to calories burned or entropy exported.
* **Top-down as constraint, not force.** Person-level ends shape which lower-level fluctuations are even recruited (constraint-closure), without “mystical causation.”
* **Plural goods, not a single currency.** Practical reason adjudicates incommensurable goods; there is no thermodynamic exchange rate between honesty and comfort.
* **Counterexamples bite:** Fasting monks, whistleblowers, and conscientious objectors can rationally *choose against* metabolic or reproductive “fitness.” The choice is **intelligible** and often **admirable**—which a mere dissipative telos can’t capture.
* **Prediction serves the chosen good.** The same predictive loop (propose→anticipate→compare→adjust) runs, but what counts as an “error” is fixed by the **practice’s** internal goods, not by system throughput.

# If you want a forum-ready clincher

> The predictive brain story is safest when we say: thermodynamics explains **how** a minded animal can exist and stay viable; **eudaimonia explains what it is for that life to go well**. Keep the “because” of enabling separate from the “because” of justifying. Then prediction is the servo of practical reason—excellent for keeping a deed apt—without collapsing the point of agency into the aims of a heat engine.

You can also frame this as a motto: **anchor in physics, answer to reasons.**

GPT-5:

Love this direction. Here’s a way to line up Peirce and Wittgenstein so they *cooperate* rather than compete—while keeping your “enablement vs. normativity” guardrail firm.

# Where the apparent disagreement comes from

* **Hanover’s point:** Wittgenstein doesn’t deny that creatures (or humans pre-linguistically) can treat smoke as a sign of fire. His focus isn’t genesis of signs but **what makes an utterance count as saying something**—public criteria, rule-governed use, “forms of life.”

* **Apokrisis’s point:** Peirce gives a **generic** account of semiosis (icon–index–symbol; habits; counterfactuals) that ranges from biology up through language. “Semiosis hinges on counterfactuality”: a sign is what it is in virtue of the *regularities it would support*—what would follow if this stood for that.

These aren’t at odds if we separate two kinds of explanations:

* **Enablement (Peirce/biosemiotics):** how a system can *come to* have signs at all—through habit formation, constraints, and counterfactual expectations in control loops.

* **Justification (Wittgenstein/socio-norms):** what makes an act *count as* following a rule, giving a reason, making a move in a game with public standards.

# How Peirce can *advance* Wittgenstein—without reduction

1. **Counterfactuals as the bridge.**

* *Peirce:* A sign’s meaning is its **would-be effects** on conduct—its habit profile (Thirdness).

* *Wittgenstein:* Rule-following requires **“there is a way of going on”**—a space of correct/incorrect continuations sustained by communal criteria.

Read together: the **habitual counterfactuals** that make a sign stable at lower levels *enable* the **normative counterfactuals** (“what would count as doing it right?”) at the public level. Same shape, different authority: habits vs. norms.

2. **Triadicity clarifies “meaning is use.”**

* Peirce’s object–sign–interpretant triangle prevents collapsing “use” into a mere stimulus–response regularity. “Use” already includes **learned generalities** (interpretants) that reach counterfactually beyond any single occasion.

* That helps explain why Wittgenstein’s **use** isn’t behaviorism: uses live inside practices whose **criteria** reach beyond the present tokening.

3. **Icon/index/symbol tracks the deer/human contrast.**

* A deer treats crackling + smoke **indexically** as danger—rich control, no public **symbolic** commitments.

* Humans stabilize **symbols**: we can be *corrected*, *justify*, *teach the rule*, *disagree while sharing the standard*. That’s the Wittgensteinian layer—**second nature**—sitting on an enabled bed of icon/index habits.

4. **Constraint-closure without downward “mysticism.”**

Peircean semiosis supplies the **control-system constraints** that make socio-normative life possible (attention, habituation, counterfactual sensitivity). Wittgenstein tells us **what counts** as a move within that life. The bottom **enables**; the top **justifies**. No competition over where normativity comes from.

# A tidy way to say it on the forum

> Peirce doesn’t replace Wittgenstein; he widens the lens. Peirce explains how signs can stably *do* work across levels—by underwriting counterfactual habits (what would follow if this stood for that). Wittgenstein explains what makes some of those sign-uses *count as* rule-following—public criteria in a form of life. The biosemiotic story **enables** socio-semiosis; the Wittgensteinian story **authorizes** it. Keep those “because”s apart and you get continuity without reduction: semiosis all the way down for control, and norms all the way up for reasons.

If you want, I can tailor a shorter reply addressed to Hanover directly (deer vs language) and another to apokrisis (on counterfactuals as the common currency) so you can drop them as targeted follow-ups.

USER:

[Quoted material snipped] If Apokrisis maintains that what we drew from the Wittgenteinian picture (regardless of any claim for intellectual priority; that wouldn't bother me if Vygotsky was credited instead) adds noting the the Peircean mix, might it be that I overlooked equivalent moves in Peirces own work? Are there places in Peirce, maybe the" pragmatist" parts, where one can find resources for actively resisting psychologism and naturalistic/genetic fallacies?

GPT-5:

Peirce already has robust, *internal* resources to block psychologism and the genetic fallacy. If you want to meet apokrisis on “Peirce alone is enough,” you can.

Here are the Peircean levers (with Peirce-ish labels you can cite):

**1) Logic as a normative science (not psychology).**
Peirce is explicit: **logic = the theory of right reasoning**, and it sits within the **Normative Sciences** (Aesthetics → Ethics → Logic). Psychology may *describe* how we do think; logic *prescribes* how we ought to reason. That’s anti-psychologism straight up.

* **Aesthetics** (for Peirce): the science of the *admirable*—sets the ultimate ideal(s).
* **Ethics:** the science of **deliberate conduct**—how to pursue those ideals.
* **Logic:** the science of **self-controlled thought**—what we ought to believe if we’re aiming at truth.

That ladder is your “order of justification” inside Peirce himself, not imported from Wittgenstein.

**2) The pragmatic maxim is anti-genetic, anti-psychologistic.**
“Consider what effects that might conceivably have practical bearings we *conceive* the object to have; our conception of those effects is the whole of our conception.”
This doesn’t say “whatever caused a concept justifies it.” It says **meaning = rule of use** (conceived consequences for action and inference). The *validity* of a belief is fixed by its *inferential/practical bearings* under disciplined inquiry, not by its psychological genesis.

**3) Community of inquiry & the “final opinion.”**
Peirce’s famous move: truth is what inquiry would converge on **in the long run** under ideal communal conditions (unlimited investigation, open criticism, etc.). That’s a **public, normative** standard. Again: not how we *do* fix belief (psychology), but how we **ought** to if we aim at truth (logic/ethics).

**4) Triadic semiosis already encodes normativity.**

* **Object – Sign – Interpretant**: The **interpretant** is (ultimately) a **habit-change**—a general rule about how one *ought* to go on in inference and conduct.
* **Final interpretant**: what would be fixed under ideal interpretation. That’s a normative target, not a causal state of a nervous system.

So the very *telos* of semiosis (Thirdness, habit, law) is normative in Peirce’s own framework.

**5) Two “because”s, now in Peirce’s voice.**
You can translate your enablement/justification split into Peircean terms:

* **Enabling-because:** categories, biosemiotic control, habit formation—why semiosis can exist and scale.
* **Justifying-because:** the normative sciences—why some inferences/conduct are *right*, given the admirable ends of inquiry and conduct.
Peirce keeps these distinct: he was a metaphysician of habit *and* a logician of norm.

**6) Where Wittgenstein is optional but helpful.**
Nothing in your argument *needs* Wittgenstein to secure normativity; Peirce already bars psychologism. Wittgenstein helps **describe** the lived grammar of rule-following and “forms of life” with a therapeutic clarity Peirce doesn’t aim for. If apokrisis wants Vygotsky/Luria/Mead instead as the socio-practical lens, fine—the key role is to keep the **public, practice-governed** character of norms in view. Peirce + (Wittgenstein *or* Vygotsky/Mead) gives you enablement **and** immanent justification without reduction.

---

### Forum-ready reply you can drop in (Peirce-only framing)

> If we stick with Peirce, we already have what we need to resist psychologism and the genetic fallacy. Peirce makes **logic a normative science**, not a branch of psychology: it prescribes how self-controlled thought *ought* to go if we aim at truth. His **pragmatic maxim** ties meaning to conceivable practical/inferential bearings (not to causal genesis), and the **community of inquiry** gives a public standard for correctness (the “final opinion” under ideal inquiry). Even triadic semiosis is norm-laden: the **interpretant** is a habit—ultimately what would guide conduct and inference if interpretation were perfected. So biosemiotic **enablement** (how signs can exist and scale) doesn’t compete with **justification** (why some moves are right); Peirce has both stories in-house. If one prefers Vygotsky/Mead for the socio-practical description rather than Wittgenstein, that’s a temperamental swap. The structural point stands: keep the causal story of how signs and habits arise distinct from the normative story of how reasons bind, and you get continuity without reduction—and a clean place to situate LLMs as artifacts that participate in semiosis without thereby acquiring the full normative standing of persons.

USER:

This now prompts my recall of some Peircean themes that were more to the fore when I was reading lots of Putnam and about his dispute with Rorty regarding the proper way to reappropriate the American pragmatists tradition of Peirce, Dewey and James. This Peircean framing of the order of justification, its source, and its independence, echoes a bit the "worlds" of Popper and Frege although the latter two were unconcerned with the ideas of material enablement/implementation, as also was Wittgenstein though the latter only needs for his account to be suitably naturalistic (in a non-objectionable sense) living bodies capable of training/acculturation. Maybe that's also all Peirce needs, and appeals to. Is it? We had seemed to supplement this picture with two ingredients: the silence-by-prediction physiological model (inspired by authors Apokrisis metioned) that bridges the enablement/control gap when glossed in terms of exaptation and the extension of the Bayesian model to Anscombean modes of practical reasoning that brings social affordances into the mix. This also brought into view within the integrated picture the distinction between two directions of subumption. So far, I only see in the pragmatist Peirce a way to indeed resist psychologism and the natural fallacy but not yet a clear account of downward causation in terms of biosemiosis. It sometimes feels to me like Apokrisis focuses on the task of explaining "real" signification, in embodied living/physiological contexts, (and how this is beyond the scope and capabilities of LLMs, while I myself find the limitations of LLMs elsewhere) in a way that locates overarching telic force in dissipative structures while showing little concern for antipsychologism. He does grant downward-causal power to signs (within a triadic theory) but not in a way that makes them reasons rather than mere motivations (again, unless I misread him). I may have missed places where he appeals to relevant features of Peirce's (or other author's) thinking to bridge the gap between pragmatism and biosemiosis. Is Peirce himself clearly bridging it?

GPT-5:

You’re circling the right spot. Short answer: yes—Peirce himself has the bridge you’re looking for, and it runs through **final causation**, **habit-taking**, and the idea that **Thirdness (law/reason)** is genuinely efficacious in nature. That’s how he links biosemiosis (embodied control) to pragmatist normativity without psychologism.

Here’s the map, in plain steps:

1. **Anti-psychologism is built in.**
For Peirce, logic is a **normative science** (nested under ethics, under aesthetics). Psychology tells us how we *happen* to think; logic prescribes how we *ought* to conduct thought if we aim at truth. So the “source of normativity” isn’t a mental state; it’s the public ideal of self-controlled reasoning (community of inquiry, long-run convergence). That already blocks the genetic fallacy.

2. **Downward causation = final causation (non-mystical).**
Peirce distinguishes **efficient causes** (pushes) from **final causes** (pulls/ends). Ends operate by **constraining** which efficient processes are recruited and stabilized. His slogan that “laws are habits” means **generalities** are real and have effects: a entrenched habit channels lower-level events. That’s precisely a principled “downward” story: not extra forces, but **top-down constraints** that make some micro-trajectories eligible and silence the rest.

3. **Habit-taking is where biosemiotics and pragmatism meet.**
A sign’s **interpretant** is (ultimately) a **habit change**—a rule for going on in inference and conduct. In organisms, habits are sensorimotor control policies; in inquiry, they are reasoning policies. Same triadic form, different grain: the **enablement** story (how sign-guided control loops exist at all) and the **justification** story (why some sign-guided moves are *right*) slot together because both are about the growth and governance of **habits**.

4. **Reasons are not “mere motivations” in Peirce.**
He defines **ethics** as the science of **deliberate self-control**, and **logic** as the ethics of thought. When an agent governs itself by an end (truthfulness, fairness, explanatory adequacy), that end is a **final cause** that shapes which impulses/evidences may count. So *reasons do cause*, but as **constraints on conduct**—they are not reducible to felt pushes. (That’s just what you’re calling “silence-by-commitment”: non-reasons are screened out because they fail the end.)

5. **Your two subsumptions land cleanly in Peirce.**

* **Cure (eudaimonia / internal goods):** Peirce’s “admirable” ideal (aesthetics) → ethics of self-control → logic of inquiry: this is the **order of justification**.
* **Curse (mere dissipative telos):** If we collapse ends into viability/throughput, we demote final causes to efficient ones and lose normativity. Peirce avoids that by insisting that **generals are real** (objective idealism) and **final causation is irreducible**.

6. **Where your predictive/Bayesian gloss fits.**
Your “silence-by-prediction” machinery is a good **implementation story** for how final causes travel down the stack: ends (final causes) **set the precision profile**—what mismatches count as errors; predictive control then **realizes** that governance in action. That is Peircean in spirit: Thirdness (law/aim) selecting and shaping Secondness (efficient interactions) against a background of Firstness (qualities/potentialities).

7. **On apokrisis’ emphasis.**
If he locates the overarching telos in “dissipative structure,” he’s giving a powerful **enabling** story. Peirce would say: good, but **don’t let it become the arbiter**. Biosemiosis without the normative sciences slides toward “motivations” only. Bring Peirce’s final causation and normative ladder back in, and you recover **reasons** as top-down constraints with real causal bite.

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### Forum-ready précis (Peirce-only, no Wittgenstein needed)

> Peirce already supplies both halves: a **biosemiotic enablement** story and a **normative justification** story. On the one hand, semiosis is triadic and causal because **final causes**—ends and habits—are real, downward-shaping constraints: they select which efficient processes get recruited (laws as habits). On the other, he puts **logic inside the normative sciences**: reasons bind as ideals of self-controlled conduct within a community of inquiry, not as psychological pushes. Put together, biosemiosis explains *how* sign-guided control loops can exist in organisms; the normative sciences explain *why* some sign-guided doings are **right**. Your predictive/Bayesian “silence-by-prediction” then reads as the **mechanism** by which final causes govern execution: ends set which errors matter; prediction makes the adjustment efficient. That’s a Peircean bridge from embodied control to rational normativity without psychologism and without reducing reasons to metabolism.