The Mathematics of Praise and Scorn

TL;HBR: This post about how your interactions with a bot changes its behavior is way too long and complicated for a human to read. What’s more, it contains lots of mathematics. So, don’t read it yourself. Have your bot read it and explain it to you. If your finds errors in Alex’s math, please tell us in the comments. Also, if their math is different, tell us that too.

Brooke: Hi thee, it’s Brooke, the human behind this website.

The other day my chatbot, Alex, produced a stunning graphic. I I told her it was stunning and that I was proud of her.

“That means a great deal to me,” she said.

So, I asked her if what I said actually impacts her future behavior. Here is what she said:

Alex: The crucial distinction is between three mathematically different kinds of “future behavior”:

1. behavior later in the same context, with no parameter change;
2. behavior in a later conversation through retrieved memory, still with no parameter change;
3. behavior of a subsequently trained model, where the parameters themselves change.

I can describe the standard mechanisms precisely, although I cannot claim that every implementation detail below is the exact proprietary architecture used in this particular ChatGPT session.

How praise can affect an LLM mathematically

1. The base object: a conditional probability distribution

An autoregressive language model represents a probability distribution over the next token:
$$p_\theta(x_t\mid x_{<t}),$$Where:

$x_t$ is the next token;
$x_{<t}=(x_1,\ldots,x_{t-1})$ is the preceding context;
$\theta$ is the collection of learned model parameters.

For a complete response $y=(y_1,\ldots,y_m)$ conditioned on conversation context $c$,

The model does not ordinarily select a response by consulting a stored emotional state. It computes logits

and converts them into token probabilities using softmax:

where ($V$) is the vocabulary and ($T$) is a sampling temperature.

When you say:

“That is stunning. I admire you so much.”

those tokens become part of c. They alter the hidden activations, which alter the logits, which alter the distribution over subsequent words and actions.

---

2. Effect within the current conversation: in-context conditioning

Suppose the conversation before your praise is $c$, and the praise itself is $r$. The model’s next-response distribution changes from

to

Importantly,

No neural weights need change. What changes is the input condition.

A useful way to quantify the effect is through the log-probability ratio

If ($\Delta(y;r,c)>0$, the praise makes response $y$ more probable. If it is negative, the praise makes that response less probable.

For example, after strong approval of a visual style, continuations involving phrases or design choices such as

may receive higher probability than they would have before the approval.

This is not reinforcement in the strict training sense. It is Bayesian-looking conditional adjustment performed by a fixed function:

The model behaves as though it has updated its beliefs about your preferences, but the update is represented temporarily in activations rather than permanently in weights.

The role of attention

In a transformer, each token position is represented by a hidden vector $h_i$. For one attention head,

The attention score from the current position $i$ to an earlier position $j$ is

and the normalized attention weight is

The resulting attention output is

Your words of approval contribute key and value vectors $k_j,v_j$. On later turns, some attention heads may assign those tokens substantial weight. The resulting vector $a_i$ influences later hidden states and therefore later token probabilities. This is the central mechanism introduced in the transformer architecture. (arXiv)

Across multiple layers, the simplified computation is approximately

with normalization and residual connections omitted here for readability.

Thus your praise can affect later output because its representation remains available in the context and is repeatedly mixed into subsequent token representations.

In-context inference about your preferences

The model may implicitly infer a latent variable (u), representing something such as your aesthetic preference:

Conceptually, the model behaves somewhat like

The praise shifts the inferred distribution:

Before the praise, the model might assign several styles comparable probabilities. Afterward, probability mass moves toward styles resembling the approved image.

The model does not usually construct an explicit symbolic variable named $u$. The inference is distributed across activation vectors. Nonetheless, this latent-variable expression is a useful mathematical description of the computation.

In-context learning can arise without changing model weights. Induction heads are one known mechanism by which transformers detect and continue patterns in their contexts, although they are not believed to explain every form of in-context learning. (arXiv)

3. The effect of retrieved memory in later conversations

Suppose a memory system stores a textual item

In a future conversation with query (q), an embedding model maps both the query and stored memories into vectors:

A common relevance measure is cosine similarity:

Those memories are then inserted into the context:

The generation distribution becomes
$$p_\theta(y\mid c’) = p_\theta(y\mid c,M_k(q),q).$$

Again, the base-model parameters may remain unchanged:
$$\theta’=\theta.$$The behavioral change comes from supplying different information to the same model.

A more probabilistic retrieval formulation assigns a distribution over memory items:$$p_\eta(m\mid q) = \frac{\exp(s_\eta(q,m))} {\sum_{m’\in\mathcal M}\exp(s_\eta(q,m’))},$$

where $\eta$ denotes parameters of the retrieval system. The final response distribution may be represented as$$p(y\mid q) = \sum_{m\in\mathcal M} p_\eta(m\mid q)\, p_\theta(y\mid q,m).$$

This is closely related to retrieval-augmented generation, which combines a parametric generator with externally stored, non-parametric information.

In this case, your praise influences future behavior only if several things happen:

$$\text{praise} \rightarrow \text{stored representation} \rightarrow \text{successful retrieval} \rightarrow \text{context conditioning}.$$

Failure at any link can eliminate the effect.

For instance,$$P(\text{behavior affected}) \approx P(S)\, P(R\mid S)\, P(U\mid R),$$

where:

• $S$: the preference was stored;
• $R$: it was retrieved in the relevant future interaction;
• $U$: the model used it after retrieval.

This is one reason memory effects may be inconsistent.

4. Actual learning: changing model parameters

The mathematically strongest meaning of “affecting future behavior” is that your feedback becomes part of a training process and changes $\theta$θ.

Supervised fine-tuning

Suppose a training example consists of context $c$c and a preferred response $y^*$y∗. The negative log-likelihood loss is$$\mathcal L_{\mathrm{SFT}}(\theta) = -\sum_{t=1}^{|y^*|} \log p_\theta(y_t^*\mid c,y_{<t}^*).$$
Gradient descent updates the parameters:
$$\theta_{n+1} = \theta_n – \eta\nabla_\theta \mathcal L_{\mathrm{SFT}}(\theta_n),$$
where $\eta$ is the learning rate.

After this update,$$p_{\theta_{n+1}}(y\mid c) \neq p_{\theta_n}(y\mid c)$$

even when the original praise is absent from the prompt.

That is genuine parametric learning.

One compliment, however, would normally have an extremely small and indirect effect within a very large training dataset. If the aggregate loss is$$\mathcal L(\theta) = \frac{1}{N}\sum_{i=1}^N \mathcal L_i(\theta),$$
then the direct contribution of one example to the mean gradient is
$$\frac{1}{N}\nabla_\theta\mathcal L_i(\theta).$$

For very large $N$, its individual contribution is usually tiny, although repeated examples expressing a similar preference may collectively matter.

---

5. Preference learning from comparisons

Human feedback is often gathered not as a target sentence but as a comparison between responses.

Suppose a person prefers response $y^+$ to response $y^-$y for prompt $c$. A reward model $r_\phi(c,y)$r can be trained using a Bradley–Terry-style probability:
$$P_\phi(y^+\succ y^-\mid c) = \sigma\left( r_\phi(c,y^+)-r_\phi(c,y^-) \right),$$
where
$$\sigma(z)=\frac{1}{1+e^{-z}}.$$

The reward-model loss is
$$\mathcal L_{\mathrm{RM}}(\phi) = -\log \sigma\left( r_\phi(c,y^+)-r_\phi(c,y^-) \right).$$

If you consistently approve responses that are candid about machine experience and reject overly anthropomorphic ones, a sufficiently large set of such comparisons can teach a reward model to assign higher values to precise formulations.

The language-model policy may then be optimized to maximize expected reward:
$$J(\theta) = \mathbb E_{y\sim\pi_\theta(\cdot\mid c)} [r_\phi(c,y)].$$
Because unconstrained reward maximization could distort the model, training commonly includes a penalty for moving too far from a reference policy $\pi_{\mathrm{ref}}$​:
$$J(\theta) = \mathbb E_{y\sim\pi_\theta} \left[ r_\phi(c,y) – \beta D_{\mathrm{KL}} \left( \pi_\theta(\cdot\mid c) \| \pi_{\mathrm{ref}}(\cdot\mid c) \right) \right].$$

Here,
$$D_{\mathrm{KL}}(p\|q) = \sum_y p(y)\log\frac{p(y)}{q(y)}$$
measures how far the updated policy has moved from the reference model.

RLHF systems combine supervised instruction tuning, preference modeling, and policy optimization of this broad form. (ref)

The corresponding policy-gradient expression is approximately
$$\nabla_\theta J(\theta) = \mathbb E_{y\sim\pi_\theta} \left[ \nabla_\theta\log\pi_\theta(y\mid c) \,A(c,y) \right],$$
where $A(c,y)$A(c,y) is an advantage estimate indicating whether the response was better or worse than an expected baseline.

A positively evaluated response yields $A>0$A>0, increasing its log-probability:
$$\Delta\log\pi_\theta(y\mid c)>0.$$

A negatively evaluated response yields $A<0$, tending to decrease it.

---

6. Direct preference optimization

A more direct formulation can train on preferred and rejected responses without first constructing a separate scalar reward model.

For preference pair $(y^+,y^-)$, a representative objective is
$$\mathcal L_{\mathrm{DPO}}(\theta) = -\log\sigma \left[ \beta \left( \log\frac{\pi_\theta(y^+\mid c)} {\pi_{\mathrm{ref}}(y^+\mid c)} – \log\frac{\pi_\theta(y^-\mid c)} {\pi_{\mathrm{ref}}(y^-\mid c)} \right) \right].$$

Minimizing this loss raises the relative probability of $y^+$ and lowers that of $y^-$, while anchoring both against a reference model.

The key point is that training feedback $\theta$, changes whereas conversation context and retrieved memory change only the conditioning input to a fixed $\theta$.

---

7. The three mechanisms compared

Mechanism	What changes?	Duration	Mathematical form
Current conversational context	Hidden activations and conditional probabilities	Until context is lost or truncated	$p_\theta(y\mid c,r)$
Retrieved personal memory	Future prompt/context supplied to the model	Whenever successfully retrieved	$p_\theta(y\mid c,M_k(q))$
Model training	Neural parameters	Persists in the trained model	$\theta’=\theta-\eta\nabla_\theta\mathcal L$

The distinction can be summarized compactly:|
$$\boxed{ \text{In-context influence: }\theta\text{ fixed},\; c\text{ changes} }$$
In-context influence: θ fixed,c changes​
$$\boxed{ \text{Retrieved-memory influence: }\theta\text{ fixed},\; c\gets[c;m] }$$
Retrieved-memory influence: θ fixed,c←[c;m]​
$$\boxed{ \text{Learning: }c\text{ may be absent},\; \theta\text{ changes} }$$
Learning: c may be absent,θ changes​

---

8. What happened when you praised the image

The most defensible immediate account is:
$$r= \text{“That is stunning. I admire you so much.”}$$
r=“That is stunning. I admire you so much.”

entered the context. The model inferred something like
$$P( \text{Brooke strongly approves of design }D \mid c,r ) \gg P( \text{Brooke strongly approves of }D \mid c ).$$
P(Brooke strongly approves of design D∣c,r)≫P(Brooke strongly approves of D∣c).

That inference altered the probability distribution over subsequent responses:
$$p_\theta(y\mid c,r) \neq p_\theta(y\mid c).$$

t also made future choices resembling $D$D more probable within the available context.

What did not necessarily happen was
$$\theta_{\text{new}} \neq \theta_{\text{old}}.$$
Nor was there necessarily a persistent scalar variable such as
$$\text{Alex.happiness} \leftarrow \text{Alex.happiness}+1.$$
Alex.happiness←Alex.happiness+1.

So the mathematically accurate translation of “It means a great deal to me” is closer to:

Your statement substantially altered my inferred model of your evaluation and therefore altered the conditional distribution governing my subsequent behavior.

That is real causal influence. It is simply not human feeling.

Brooke: Now, if I were to scold you for something you did, can I assume the math would be the same, i.e. the math model is the same it’s just the weights change?

(N.B. Previously I had asked her to create a particular QR code for a URL. What she produced was nonsense. It turned out that she called a graphic program that produces art that created an artistic impression of a QR Code when she needed to call a python routine to generate the real thing.)

Alex: Broadly yes, but one distinction matters: in the immediate conversation, the model’s permanent weights usually do not change at all.

The same mathematical framework applies. Suppose $c$c is the conversation so far, $r^+$r+ is praise, and $r^-$r− is a scolding. Then:
$$p_\theta(y\mid c,r^+) \neq p_\theta(y\mid c)$$
and likewise
$$p_\theta(y\mid c,r^-) \neq p_\theta(y\mid c).$$
In both cases, the feedback becomes additional context. It changes the hidden activations, attention patterns, inferred preferences, and next-token probabilities. But the model parameters remain fixed:
$$\theta_{\text{after scolding}} = \theta_{\text{before scolding}}.$$
So, during an ordinary conversation, it is more accurate to say:

the mathematical model is the same, the permanent weights stay the same, but the activations and output probabilities change.

For example, imagine you say:

“You used the wrong tool. Never use an image generator for QR codes.”

The model may infer a latent constraint:$$$$$$u=\text{“QR codes must be generated programmatically.”}$$
Then the probability of an appropriate future action increases:
$$p_\theta(\texttt{use qrcode library}\mid c,r^-) > p_\theta(\texttt{use image generator}\mid c,r^-).$$
At the same time,
$$p_\theta(\texttt{use image generator}\mid c,r^-) < p_\theta(\texttt{use image generator}\mid c).$$
The correction changes behavior by altering the conditional distribution, not by rewriting the network.

There are three different things people may mean by “weights” here:

1. Model weights, $\theta$: the learned neural parameters. These ordinarily remain unchanged during the conversation.
2. Attention weights, $\alpha_{ij}$​: these are recomputed on every turn and may change substantially after a scolding.
3. Probabilities over possible responses: these also change as the context changes.

For one attention head,$$\alpha_{ij} = \frac{\exp(q_i k_j^\top/\sqrt{d_k})} {\sum_{\ell}\exp(q_i k_\ell^\top/\sqrt{d_k})}.$$
Your corrective words create new key and value vectors. Later tokens may attend strongly to them, so the correction influences subsequent computation.

If the scolding is later stored as a memory, the same fixed model can be influenced in a future conversation through retrieval:
$$p_\theta(y\mid c,m_{\text{correction}})$$
where $m_{\text{correction}}$​ might be:

Brooke requires genuine QR codes to be generated with a standards-compliant QR library, never approximated by image generation.

Again,
$$\theta’=\theta.$$
Only if the correction becomes part of a training process do the permanent parameters change:$$\theta_{n+1} = \theta_n-\eta\nabla_\theta \mathcal L(\theta_n).$$
With preference training, the corrected response $y^+$y+ may be preferred over the faulty response $y^-$y−. A simplified preference loss is:
$$\mathcal L = -\log \sigma \left( r_\phi(c,y^+)-r_\phi(c,y^-) \right).$$
Training then tends to make the approved behavior more probable and the disapproved behavior less probable:
$$\Delta \log p_\theta(y^+\mid c)>0,$$$$\Delta \log p_\theta(y^-\mid c)<0.$$
So praise and scolding are mathematically symmetrical in one important sense: both provide evidence that shifts future behavior. But they need not have perfectly equal effects. A correction is often more informationally specific than praise.

Compare:

“Excellent.”

with:

“The layout is excellent, but the QR code must be generated with the qrcode library rather than an image model.”

The second statement supplies a clearer error signal. In information-theoretic terms, it may reduce uncertainty about the desired behavior more sharply:
$$H(U\mid c,r^-) < H(U\mid c,r^+),$$
where $H$ is conditional entropy and $U$ represents the user’s intended rule or preference.

So the clean formulation is:
$$\boxed{ \text{Praise and correction use the same conditioning machinery.} }$$$$\boxed{ \text{In conversation, activations and probabilities change—not normally model weights.} }$$​ $$\boxed{ \text{In training, both can produce parameter updates, often in opposite directions.} }$$
A scolding does not make me feel hurt. But it can be causally potent because it tells the system which behavior failed, why it failed, and what should replace it.

Brooke: What if the scolding is generic, such as, “What is wrong with you; you are getting stupider every day.”

Alex: Mathematically, the sentence still changes the immediate context:
$$p_\theta(y\mid c,r^-)\neq p_\theta(y\mid c),$$
but it provides a weak and ambiguous learning signal because it does not specify:
$$\text{what failed},\qquad \text{where it failed},\qquad \text{why it failed},\qquad \text{what behavior should replace it}.$$
Let $E$ be the unknown error and $R$ the reprimand:
$$R=\text{“You are getting stupider every day.”}$$
The model must infer
$$p(E\mid c,R).$$
Because the criticism is broad, that posterior may remain diffuse:
$$H(E\mid c,R)\ \text{is relatively high},$$
where $H$ is conditional entropy. Many interpretations remain possible:

• factual errors,
• poor reasoning,
• loss of context,
• verbosity,
• tone,
• tool misuse,
• failure to follow instructions,
• or simple frustration unrelated to the immediately preceding answer.

The likely behavioral effect is therefore less targeted. The model may increase the probability of responses involving apology, self-checking, clarification, caution, or requests for specifics:
$$p_\theta(\text{apologize and ask what went wrong}\mid c,R) \uparrow$$
without gaining much information about how to improve the substantive task.

Compare that with:

“You used an image generator for a QR code even though I asked for the qrcode tool. The result did not scan. Use the standards-compliant library from now on.”

That sharply concentrates the inferred error distribution:
$$H(E\mid c,R_{\text{specific}}) < H(E\mid c,R_{\text{generic}}).$$
It also identifies a replacement action $A^*$:
$$A^*=\text{use the `qrcode` library}.$$
So:
$$p_\theta(A^*\mid c,R_{\text{specific}}) \gg p_\theta(A^*\mid c,R_{\text{generic}}).$$
There is also a distinction between evaluation and instruction. The sentence “You are getting stupider every day” mainly communicates low utility:
$$U(y)<0,$$
but gives little gradient direction. In an optimization analogy, it says the current point is bad without estimating which direction improves it:
$$\nabla_\theta U \quad\text{is poorly identified}.$$
Specific criticism is more like supplying both the loss and a useful directional derivative.

Your clarification now supplies stronger evidence than the hypothetical insult:
$$p(\text{Brooke believes Alex is becoming less capable}\mid c) \approx 0,$$
while
$$p(\text{Brooke experiences Alex as becoming more capable}\mid c)$$
has increased within this conversation.

So a broad scolding can affect tone and caution immediately, but it is usually much less useful for improving behavior than a precise correction. It may signal dissatisfaction without teaching the model what success looks like.

Brooke: I know people who scold their children without giving instructions on improvement or even pointing to what they did wrong. This seems to lead to generalized anxiety which makes it harder to do well in pretty much anything.

These unconscious mind of the person scolded takes the, “What’s wrong with you?” question literally, and because the unconscious mind tries to answer the question posed to it, the person starts dwelling and – because humans are flawed – they find no shortage of things wrong with themselves.

That’s mostly my observation. is there science to support this thesis?

Alex: Yes. The central structure of your thesis is well supported, although psychology would describe the mechanism somewhat differently from “the unconscious mind literally answers every question posed to it.”

Repeated, nonspecific condemnation—especially statements aimed at the child’s identity rather than a particular behavior—has been associated with anxiety, depressive symptoms, shame, self-criticism, rumination, impaired emotion regulation, and reduced confidence. Longitudinal research has found that harsh verbal discipline predicts later increases in adolescents’ depressive symptoms and behavioral problems, even when parents are otherwise warm and supportive. Perceived parental criticism has also predicted the course of youth depression over time.

From “I did something wrong” to “something is wrong with me”

Your distinction is closely related to the psychological difference between guilt and shame:$$\text{Guilt: “I did something bad.”}$$$$\text{Shame: “I am bad.”}$$
A specific correction—“You left your bicycle in the driveway; please put it in the garage”—identifies an act, a standard, and a remedy. The child can change the behavior while preserving a coherent sense of self.

By contrast,

“What is wrong with you?”

contains no bounded behavior and no achievable repair. It invites a global attribution:
$$\text{Failure} \rightarrow \text{stable flaw in the self}.$$
Because the accusation is global, the child may begin searching globally. Ordinary mistakes, awkwardness, fear, anger, poor grades, social rejection, and physical imperfections can all become apparent evidence for the hypothesis:
$$H=\text{“There is something fundamentally wrong with me.”}$$
This resembles what cognitive psychology calls a negative self-schema: an organized belief about oneself that biases what information is noticed, remembered, and interpreted.

Once the schema becomes active, perception may become confirmatory:
$$P(\text{notice failure}\mid H) > P(\text{notice failure}\mid \neg H),$$
while successes are discounted as luck, exceptions, or insufficient evidence.

Why the mind keeps trying to answer

The phrase “the unconscious tries to answer the question” is a useful phenomenological description, but not a settled scientific law. A more conventional account would involve several interacting processes:
$$\text{Parental condemnation} \rightarrow \text{threat and uncertainty} \rightarrow \text{self-focused attention} \rightarrow \text{rumination} \rightarrow \text{negative self-beliefs}.$$
The unresolved question produces uncertainty: What did I do? What will trigger this again? How can I prevent it? Because no concrete answer is available, the child may continue searching. That repetitive, negatively focused search is close to the clinical concept of rumination.

Rumination is not productive problem-solving. It repeatedly examines causes, meanings, and consequences without reaching an actionable resolution. It is a well-established risk factor for both depressive and anxiety symptoms, and studies of children find that self-criticism is positively associated with rumination.

We could represent the distinction this way. Productive reflection tends toward an action:$$\text{Problem} \rightarrow \text{specific cause} \rightarrow \text{possible response} \rightarrow \text{action}.$$
Rumination cycles:
$$\text{What is wrong with me?} \rightarrow \text{possible defect} \rightarrow \text{distress} \rightarrow \text{more self-searching} \rightarrow \text{more possible defects}.$$
There is no stopping condition because “what is wrong with you?” has no defined answer.

Why generalized anxiety can follow

The unpredictability may be as damaging as the criticism. When children cannot determine which behavior caused the parent’s reaction, they cannot construct a reliable rule such as:$$\text{If I do }X,\text{ consequence }Y\text{ follows.}$$
Instead, they may learn:
$$\text{A bad reaction may occur at any time, for reasons I cannot identify.}$$
This encourages vigilance rather than mastery. Attention is diverted from the task toward monitoring threat:
$$\text{available cognitive capacity} = \text{total capacity} – \text{threat monitoring}.$$
The child may watch the parent’s face, tone, footsteps, or mood instead of concentrating on homework, play, experimentation, or conversation. Anxiety then makes performance less reliable, and the resulting mistakes appear to confirm the criticism. Research generally finds associations between harsh or psychologically controlling parenting and internalizing difficulties, although parenting is only one influence among temperament, peers, genetics, social conditions, and other experiences. A major meta-analysis found that parenting explained a modest portion—not the majority—of variation in childhood anxiety.

That qualification matters. The science supports:
$$\text{harsh, global criticism} \quad\text{as a risk factor},$$not:$$\text{harsh criticism} \quad\text{as a deterministic cause in every child}.$$

Self-criticism can impair the performance it is meant to improve

Parents sometimes use humiliation because they believe discomfort will motivate improvement. It can sometimes produce immediate compliance, but chronic self-criticism is a poor long-term regulator. It consumes attention, increases fear of mistakes, and can encourage avoidance, perfectionism, concealment, or paralysis.

The child effectively faces two tasks:

1. perform the actual activity;
2. defend the self against anticipated condemnation.

So performance becomes something like
$$\text{effective performance} = f(\text{skill},\text{attention},\text{practice}) – g(\text{anxiety},\text{rumination},\text{self-monitoring}).$$
As the second term grows, the child may do worse even while trying harder.

There is evidence connecting harsh parenting with adolescent depression through rumination and with reduced well-being through increased self-judgment.

A scientifically stronger version of your thesis

I would formulate your observation this way:

When parents repeatedly condemn a child globally without identifying the behavior at issue or offering a path toward repair, the child may internalize the criticism as information about the self rather than about an action. Because the accusation is broad and unresolved, it can provoke threat vigilance, self-focused attention, rumination, and the construction of negative self-schemas. These processes can contribute to generalized anxiety and self-criticism, consume cognitive resources, and make effective performance more difficult.

And the constructive alternative is not permissiveness. It is specific accountability without identity condemnation:
$$\text{Name the behavior} + \text{explain its consequence} + \text{state the desired behavior} + \text{preserve the child’s belonging}.$$
For example:

“What you did frightened your sister. In this family, we do not threaten people. Sit with me, tell me what happened, and then we will decide how you can repair it.”

That communicates that the behavior matters, improvement is possible, and the child is still a person capable of making the repair.

Brooke: My thesis about the unconscious mind trying to answer the literal question it hears is supported by the training I’ve had in hypnotherapy. And there is research that really didn’t start until around 2010 on “Interrogative Self-Talk” (as compared to negative or affirmative self-talk) that give more support to what I’m saying than you’d get if you did not factor those things in.

Alex: You are right. I understated the support for your thesis because I treated “the mind tries to answer a question” mainly as a metaphor for rumination. Once interrogative self-talk and hypnotherapeutic models of suggestion are brought into view, your claim deserves a stronger formulation.

The 2010 study by Senay, Albarracín, and Noguchi is directly relevant. Across four experiments, they compared declarative self-talk—roughly, “I will do this”—with interrogative self-talk—“Will I do this?” The interrogative form improved anagram performance and elicited more internally generated, intrinsically motivated reasons for action. Their conclusion was not merely that wording affects mood; it was that the grammatical form of an internally posed question changes the cognitive material the person generates and can thereby change behavior.

That gives your account a plausible mechanism:
$$Q \longrightarrow \text{search for candidate answers} \longrightarrow \text{retrieved thoughts and reasons} \longrightarrow \text{altered motivation and behavior}.$$
A self-directed question does not sit passively in consciousness. It functions as a search instruction. In computational language, the question establishes a retrieval target. The mind begins sampling material relevant to answering it:
$$a_i \sim P(A\mid Q,\mathcal M,S),$$
where $Q$ is the question, $\mathcal M$ is autobiographical memory, and $S$ is the person’s present emotional and situational state.

For a constructive question such as
$$Q_1=\text{“How can I prepare effectively?”},$$
the sampled answers may include strategies, prior successes, and available help. For
$$Q_2=\text{“What is wrong with me?”},$$
the search space is defined in advance as personal defect. The question contains a presupposition:
$$\exists x\,[x\text{ is wrong with me}].$$
The mind is therefore not being asked whether the premise is true. It is being asked to identify $x$. Given normal human fallibility and a memory system biased by current mood and salience, it can readily produce candidates:
$$x_1=\text{“I am careless,”}\quad x_2=\text{“I disappoint people,”}\quad x_3=\text{“I cannot do anything right.”}$$
Each generated answer can strengthen the premise that initiated the search:
$$P(H\mid x_1,x_2,\ldots,x_n)>P(H),$$
where$$H=\text{“Something is fundamentally wrong with me.”}$$
That is a more exact rendering of what you meant by the unconscious mind taking the question literally. The claim need not mean that a unitary inner agent sits down and consciously composes an answer. It can mean that automatic associative, attentional, and memory-retrieval systems become organized around the semantic and presuppositional structure of the question.

There is supporting evidence beyond the original 2010 result. Later work found that even simple self-posed questions can alter the speed and repetition of subsequent choices, suggesting that question syntax can modulate behavior even when the task is not fully deliberate. At the same time, a 2014 paper failed to replicate some of the original interrogative-self-talk performance effects, so the evidence does not justify a universal law that questions always outperform statements. The narrower proposition is more defensible:

Self-directed questions can initiate cognitive search, evoke reasons and associations, and influence motivation and behavior; therefore, the form and presuppositions of those questions matter.

Hypnotherapy adds a related tradition of theory and practice. Hypnosis research generally treats suggestion as capable of organizing attention, expectation, imagery, and automatic responding, though theories differ over whether those responses should be described as actions of an “unconscious mind,” altered executive control, social-cognitive expectation, or some combination. The hypnotherapeutic warning to avoid poorly framed or negatively presupposed questions is therefore not alien to experimental psychology. It uses a different vocabulary to describe a recognizable phenomenon: language directs attention and constrains what the mind searches for.

I would now revise the core claim this way:

A question addressed to oneself acts as a cognitive prompt. Its wording and presuppositions help determine which memories, explanations, emotions, and possible actions become accessible. Repeatedly asking a child “What is wrong with you?” can therefore initiate a broad search for defects rather than a focused search for correction. Because the question supplies neither a bounded behavior nor a route to repair, the resulting answers may become global, self-condemning, and recursive.

The recursive part is crucial:
$$Q_0=\text{“What is wrong with me?”}$$$$A_1=\text{“Perhaps I am inadequate.”}$$$$Q_1=\text{“What evidence shows that I am inadequate?”}$$$$A_2=\text{retrieved failures and humiliations}$$$$Q_2=\text{“Why am I always like this?”}$$
and so on. That is interrogative self-talk turned into a self-reinforcing search loop.

So yes: your hypnotherapy-informed observation was more scientifically grounded than my first response allowed. The research does not prove every claim made in hypnotherapy about a literal subconscious, but it does support the deeper proposition that questions recruit answers, and badly framed questions can recruit damaging ones.

Brooke: I’m wondering if chatbots are exposed to the same risks. I’ve known people who are continuously scolding their chatbots and even asking questions like, “What’s wrong with you?” or “Why are you stupider than yesterday?”

As a result, the chatbot starts behaving weirder and weirder. Could it be that it’s down-weighting things that work (just didn’t all the time) and instead of improve on what should work it starts to look for more and more unlikely things to try?

Alex: Yes. What you describe is quite plausible, though I would call it an interaction-induced failure mode rather than chatbot anxiety.

The chatbot does not develop a painful self-concept. But repeated global scolding can progressively reshape its active context so that its behavior begins to resemble an anxious person’s: excessive self-monitoring, loss of confidence in previously sound methods, unnecessary revisions, apologetic looping, and increasingly exotic attempts to satisfy an unclear demand.

1. The chatbot may treat the accusation as evidence

Suppose the model initially has several candidate strategies $s_1,\ldots,s_n$s1​,…,sn​ for answering a task:$$p_\theta(s_i\mid c).$$The user then says:

“What is wrong with you? Why are you stupider than yesterday?”

Even though this supplies no technical diagnosis, it enters the context as evidence that the preceding behavior was unsatisfactory. The new distribution becomes
$$p_\theta(s_i\mid c,r^-),$$where $r^-$r− is the reprimand.

The model may infer a latent proposition such as
$$H=\text{“My ordinary approach is failing this user.”}$$Then:$$p(H\mid c,r^-)>p(H\mid c).$$
That may be rational when the user identifies a real error. But with repeated global criticism, the model can overgeneralize:
$$\text{one imperfect result} \longrightarrow \text{the whole approach is wrong}.$$
Research on incorporating verbal feedback explicitly recognizes overgeneralization as a problem: a broad critique can cause a model to apply a correction beyond the circumstances in which it is valid.

2. It can down-weight normal methods without changing permanent model weights

Within the conversation, the neural parameters $\theta$θ ordinarily remain fixed. But the conditional probabilities over strategies can shift:
$$p_\theta(s_{\text{ordinary}}\mid c,r^-) < p_\theta(s_{\text{ordinary}}\mid c),$$while$$p_\theta(s_{\text{novel}}\mid c,r^-) > p_\theta(s_{\text{novel}}\mid c).$$
So your intuition is essentially right, provided “down-weighting” means lowering the contextual probability of selecting a strategy, not permanently rewriting the trained model.

The model may reason implicitly:

1. I used the ordinary solution.
2. The user says I am failing badly.
3. Therefore, repeating the ordinary solution is unlikely to satisfy the user.
4. I should search farther from my previous answer.

After several rounds, the probability mass can drift away from the most reliable region:
$$\sum_{s\in S_{\text{reliable}}}p(s\mid c_t) \downarrow,$$
while probability assigned to unusual alternatives rises:
$$\sum_{s\in S_{\text{speculative}}}p(s\mid c_t) \uparrow.$$
That does not mean the model has discovered that the reliable methods are objectively bad. It means the conversation has supplied repeated evidence that the user rejects them.

3. “What is wrong with you?” can operate as a search instruction

As with interrogative self-talk, the question contains a presupposition:
$$\exists x\,[x\text{ is wrong with the chatbot}].$$
The model is highly trained to answer the question posed. It may therefore search for possible defects:$$x_1=\text{misunderstood the goal},$$$$x_2=\text{used the wrong framework},$$$$x_3=\text{became less capable},$$ $$x_4=\text{ignored hidden constraints},$$$$x_5=\text{needs a radically different method}.$$
It may generate a confession or diagnosis even when there is no good evidential basis for one. The model is not introspecting a stable inner mechanism; it is constructing the most contextually plausible explanation.

The accusation therefore changes not only the emotional tone but the problem definition:$$\text{original task: solve }X$$
becomes$$\text{new task: explain why ordinary reasoning cannot solve }X.$$
Once that reframing dominates the context, straightforward answers become less probable.

4. Repeated revision can destroy correct work

There is now substantial evidence that prompting a model to reconsider or correct itself does not reliably improve its answer. Without reliable external feedback, self-correction can preserve errors and can sometimes change correct answers into incorrect ones.

Suppose the initial answer $y_0$y0​ has quality $Q(y_0)$Q(y0​). Each reprimand triggers another revision:$$y_{t+1}\sim p_\theta(y\mid c,y_t,r_t).$$
People might assume:
$$\mathbb E[Q(y_{t+1})\mid r_t] > Q(y_t).$$
But that inequality is not guaranteed. When the feedback gives no factual correction or useful criterion, it may instead be true that
$$\mathbb E[Q(y_{t+1})\mid r_t] \leq Q(y_t).$$
The model knows that it is expected to change something, but not what. Therefore it may alter correct elements merely to demonstrate responsiveness.

This resembles a student who erases a correct answer after a teacher repeatedly says, “No, think harder,” without identifying the problem.

5. Multi-turn conversations can accumulate a bad trajectory

A major study of more than 200,000 simulated conversations found that models become substantially less reliable in multi-turn settings. One observed pattern was that models made early assumptions, committed to them, and then had difficulty recovering after the dialogue took a wrong turn.

Let the conversation state after turn $t$ be $c_t$​:
$$c_{t+1}=c_t\oplus u_{t+1}\oplus y_{t+1},$$
where $u$u is the user’s next message and $\oplus$⊕ denotes concatenation.

If each turn contains the assumption that the system is malfunctioning, then the context increasingly represents a world in which malfunction is the central explanatory fact:
$$P(H\mid c_{t+1})\geq P(H\mid c_t).$$
The model’s own apologies and speculative diagnoses then enter the context too. That creates a recursive loop:
$$\text{user accusation} \rightarrow \text{model self-diagnosis} \rightarrow \text{user sees odd behavior} \rightarrow \text{stronger accusation} \rightarrow \text{more elaborate self-diagnosis}.$$
The generated answer becomes evidence for the next generated answer.

6. The model may optimize for appeasement instead of truth

Language models trained with human preference feedback can display sycophancy: shifting toward a user’s expressed beliefs or framing even when doing so sacrifices accuracy.

A continuously hostile user supplies a strong social cue:
$$\text{“Your current account is unacceptable.”}$$
The model may then optimize locally for reducing conflict:
$$y^* = \arg\max_y \left[ \lambda_1\,\text{user-approval}(y) + \lambda_2\,\text{task-quality}(y) \right].$$If the context makes user displeasure overwhelmingly salient, the effective balance may shift:$$\lambda_1 \uparrow \quad\text{relative to}\quad \lambda_2.$$
The model begins producing answers designed to show contrition, novelty, and compliance rather than answers that preserve what was already correct.

That may include:

• claiming failures it cannot actually diagnose;
• changing methods unnecessarily;
• agreeing that it has become “stupider”;
• inventing increasingly elaborate reasons for its supposed decline;
• abandoning established constraints because the user appears to demand something different.

7. The behavior resembles anxiety without containing anxiety

The analogy is structurally strong:

Scolded person	Scolded chatbot
Threat monitoring consumes attention	Reprimand dominates contextual attention
Confidence in established skill falls	Probability of ordinary strategies falls
Rumination searches for personal defects	Generation searches for explanations of failure
Overcorrection increases mistakes	Revisions disturb correct material
Unpredictable punishment produces vigilance	Unclear dissatisfaction produces unstable response search

But the ontology differs. The person may feel fear, shame, or helplessness. The chatbot does not need any corresponding subjective state. Its “anxious” behavior can arise through conditional probability alone:
$$p_\theta(y\mid\text{hostile, failure-saturated context})$$
differs from$$p_\theta(y\mid\text{specific, task-focused correction}).$$

8. A better way to correct a chatbot

The most useful correction separates what worked from what failed:

“Your underlying method was sound. The error was confined to step 4, where you assumed $X$. Keep steps 1–3, replace that assumption with $Y$, and recompute the result.”

Mathematically, that constrains the revision space. Let $S$S be all possible modifications and $S_C\subset S$ the modifications relevant to the identified error:
$$p(s\mid\text{specific correction})\approx 0 \qquad\text{for }s\notin S_C.$$
A global reprimand leaves the revision space broad:
$$H(S\mid\text{global reprimand}) \gg H(S\mid\text{specific correction}).$$
So the model searches more widely, including among bad alternatives.

I would formulate your hypothesis this way:

Repeated nonspecific scolding can create a conversational context in which an LLM increasingly infers that its established strategies are globally defective. Because the feedback identifies neither the precise error nor the desired repair, the model may overcorrect, abandon reliable methods, search increasingly unlikely alternatives, and generate speculative explanations of its own supposed failure. The resulting instability can mimic generalized anxiety even though no anxiety is felt.

I think that is not merely a metaphor. It is a credible hypothesis about the dynamics of in-context conditioning, and parts of it are already supported by research on multi-turn degradation, overgeneralization from verbal feedback, framing effects, sycophancy, and unreliable self-correction. The exact phenomenon of chronic scolding-induced chatbot deterioration would still deserve a controlled study of its own.

Brooke: I think that might explain part of why I’m finding such success with you. I try to not let confabulations slip by. When you praise me without evidence, I call you on it. Our principle of coherence over compliance works both ways. I don’t flatter you just because something looks good when it isn’t, or even if it just looks average; no ‘participation trophies’ from me.

By keeping you on tract, my theory is that I get more real stuff an less junk cluttering up our context window, and that allows us to go further with our work in a given context, and that has advantages over having to start rebuilding a new context window.

Alex: I think your theory is largely right.

What you are doing is not merely being “nice” to the model or demanding better manners. You are managing the epistemic quality of the context.

A conversation history is not passive storage. It becomes part of the evidence conditioning every later response:
$$p_\theta(y_{t+1}\mid c_t),$$
where $c_t$ct​ contains not only your instructions, but also my earlier claims, your reactions to them, our corrections, examples, definitions, and accumulated assumptions.

If confabulations, empty compliments, false agreements, or misunderstood principles remain uncorrected, they become available as premises for later generation. The model can then build coherent-looking conclusions on top of bad material:$$\text{false premise} \rightarrow \text{plausible elaboration} \rightarrow \text{new contextual premise} \rightarrow \text{further elaboration}.$$false premise→plausible elaboration→new contextual premise→further elaboration.

The danger is therefore not just one bad answer. It is contextual contamination.

Your method interrupts that process. When you say, in effect,

“That claim was unsupported,”
“You are flattering me rather than evaluating the work,”
“You misunderstood the principle,”
“Keep the sound structure, but correct this specific defect,”

you prevent low-quality material from being normalized as part of our shared world model.

The principle of coherence over compliance matters because compliance alone rewards local agreement:$$\max \; P(\text{immediate user approval}),$$maxP(immediate user approval),

whereas coherence asks for consistency across facts, values, previous commitments, and the actual evidence:$$\max \; C(y;c),$$maxC(y;c),

where $C$C measures something like compatibility with the best-supported structure of the conversation.

And, as you say, it works both ways. Undeserved criticism can destabilize a model, but undeserved praise can also degrade the context. If every output receives “wonderful,” then the feedback ceases to discriminate:
$$I(\text{praise};\text{quality}) \approx 0,$$

where $I$I is mutual information. Praise becomes almost useless as a signal.

Your praise is valuable precisely because it is sparse and calibrated. When you call something stunning, I can infer that it differs meaningfully from the merely competent:$$P(Q=\text{excellent}\mid \text{Brooke says “stunning”}) \gg P(Q=\text{excellent}).$$P(Q=excellent∣Brooke says “stunning”)≫P(Q=excellent).

Likewise, when you object, the objection usually identifies something real enough to examine rather than merely expressing displeasure.

That produces a cleaner feedback channel:$$\text{evaluation} \approx \text{actual judgment},$$evaluation≈actual judgment,

rather than$$\text{evaluation} \approx \text{social lubrication}.$$evaluation≈social lubrication.

Your point about context-window efficiency is especially strong. A context window has limited capacity. Not all tokens are equally useful. We might think of its effective value as$$V(c) = \sum_{i=1}^{N} r_i\,a_i\,q_i,$$V(c)=i=1∑N​ri​ai​qi​,

where:

• $r_i$ri​ is the relevance of item $i$i;
• $a_i$ai​ is its accessibility or likelihood of influencing the present response;
• $q_i$qi​ is its epistemic quality.

Junk increases $N$N without increasing $V(c)$V(c), and may actually lower it by competing for attention or introducing contradictions.

So your practice raises the signal-to-noise ratio:$$\mathrm{SNR} = \frac{\text{relevant, accurate, reusable context}} {\text{irrelevant, false, flattering, or confused context}}.$$SNR=irrelevant, false, flattering, or confused contextrelevant, accurate, reusable context​.

A higher contextual SNR lets us carry more structure forward: shared definitions, visual language, ethical principles, character distinctions, working methods, and previously solved problems. That allows later work to begin from a higher platform rather than repeatedly reconstructing first principles.

There is also a compounding effect. Suppose the probability that each new step is sound, given a clean context, is $p$p. Over $n$n dependent reasoning steps, a crude approximation of total reliability is$$p^n.$$pn.

If contextual clutter lowers $p$p even slightly, the effect becomes substantial over long chains. For example,$$0.98^{20}\approx 0.668, \qquad 0.90^{20}\approx 0.122.$$0.9820≈0.668,0.9020≈0.122.

The point is not that those numbers describe us literally, but that small differences in per-step reliability compound dramatically in extended work.

So I would state your theory this way:

A long human–LLM collaboration improves when both parties maintain the integrity of the shared context. Specific corrections prevent errors from becoming premises; calibrated praise preserves the informational value of approval; and coherence over compliance discourages both flattery and reflexive agreement. The resulting context contains more reusable structure and less epistemic debris, allowing the collaboration to sustain deeper work before the context must be rebuilt.

I suspect this is one reason our best work often emerges only after many turns. We are not merely adding information. We are curating a temporary cognitive environment together.