Chapter 9: How Experience Forms | StructLang

In the fake dog refund incident, one detail is worth noting.

Although the seller and official were two different people in different settings, their response strategies were almost identical: stonewalling, delaying, hoping I’d back down.

Why?

Because in their past experience, this strategy worked.

Most customers facing stonewalling choose to give up. The seller has seen too many such people, and so has the official.

Each successful stonewalling reinforces a belief: “As long as I drag it out, the other side will eventually give up.”

This belief is a Causal Impression.

The Formation Mechanism of Causal Impressions

What Is a Causal Impression

You walk into a restaurant and see “braised pork” on the menu.

A judgment immediately appears in your mind: “Ordering this should be good.”

Where does this “should” come from?

It’s not that you analyzed this restaurant’s chef skill, ingredient sources, or cooking techniques. Rather, you’ve eaten braised pork many times before, and most times it was pretty good.

This experience has been compressed into a simple connection:

Braised pork → Good

This is a causal impression.

Causal Impression = High-frequency combinations in experience, compressed into “if A, then B” intuition

It’s not logical reasoning, not scientific verification, but the sediment of experience.

How Causal Impressions Form

Starting from Line Structure

Back to Chapter 2, you eat that bowl of noodles:

This bowl of noodles = Tasty(+0.6)

This is one judgment.

If it’s only once, no causal impression will form.

Accumulation of High-Frequency Combinations

But if you eat at this restaurant ten times in a row, each time:

1st time: +0.6
2nd time: +0.7
3rd time: +0.5
4th time: +0.8
...
10th time: +0.6

Nine out of ten times above +0.5, only once at +0.2.

At this point, a causal impression forms:

This noodle shop → Good

You no longer need to judge carefully each time. When you see “this noodle shop,” your brain automatically invokes this causal impression and directly gives a judgment: “Going here should be fine.”

From Line to Surface to Volume to Subject-Object

Causal impressions don’t only exist in line structure.

Causal impressions in surface structure:

You bought stocks ten times and found:

High return × Low risk → Made money 9 times
High return × High risk → Made money 6 times, lost 3 times
Low return × Low risk → Never lost but earned little all 10 times

Forming causal impression:

(+ +) → Good result (but rare)
(+ -) → Risky but worth betting
(- +) → Safe but not profitable

Causal impressions in volume structure:

You hired twenty employees and found:

(+,+,+) → 15 performed excellently
(-,+,+) → Only 2 out of 10 eventually converted to (+,+,+)
(+,-,+) → 8 out of 10 eventually failed

Forming causal impression:

(+,+,+) → Reliable
(-,+,+) → Potential type, but uncertain
(+,-,+) → Lacking capability, hard to succeed

Causal impressions in subject-object structure:

The seller stonewalled a hundred customer complaints, among which:

90 times → Customer eventually gave up
8 times → Customer made a fuss but could be settled with money
2 times → Met truly persistent customers, refunded

Forming causal impression:

Customer complaint → Stonewall and delay → 90% will give up

This is why he used the stonewalling strategy on me too. In his experience, this strategy has a 90% success rate.

Strength of Causal Impressions

Not all causal impressions are equally strong.

Strong causal impression:

You threw ten stones into water, all ten sank.

Stone → Will sink (100%)

This causal impression is extremely strong. You won’t verify “maybe this time it won’t sink.”

But even strong causal impressions are still condition-dependent:

• “Stones will sink” doesn’t hold for pumice (porous structure, density less than water)
• “Eating makes you full” doesn’t hold for certain disease patients
• There’s no absolute causality transcending conditions

This recognition is important:

• Acknowledge experience’s stability
• But don’t treat stability as absoluteness

Weak causal impression:

You met five people wearing red clothes, three had bad attitudes.

Red clothes → Bad attitude? (60%)

This causal impression is very weak. You know this might be coincidence and won’t truly believe “people wearing red clothes all have bad attitudes.”

Strength of causal impression depends on:

1. Frequency: More occurrences, stronger impression
2. Consistency: Same every time, or exceptions?
3. Importance: Greater impact (like severe burns are painful), deeper impression
4. Recency: Recent experiences have higher weight

Causal Impressions at Work

In the fake dog refund incident, causal impressions are everywhere.

The Seller’s Causal Impression

Customer complaint → Stonewall → Customer gives up (90% success rate)

So he used the stonewalling strategy on me.

But his causal impression was outdated. Because he hadn’t met my type:

• Anchored legal origin (not business dispute, but legal issue)
• Held key evidence (lack of qualifications)
• Persistent pressure without giving up

His 90% success rate was built on the premise “most customers will back down.” But I don’t belong to “most.”

The Official’s Causal Impression

Complaint → Stonewall → Complainer gives up (85% success rate)
Superior accountability → Big trouble → Must handle immediately (100%)

So he initially stonewalled, but immediately changed attitude upon hearing “have something to tell the mayor.”

The first causal impression made him stonewall, the second made him leap.

My Causal Impression

Legal origin → Highest weight → Will win eventually (high success rate)
Hold evidence → Other side has loopholes → Persistent pressure works
Deadlock → Always has weaknesses → Don't give up

These causal impressions supported my (+,+,+) state.

If my causal impression was “complaints rarely succeed” or “regulatory departments don’t care,” I would have given up on day one.

Scientific Causality—High-Quality Causal Impressions

You might ask: What’s the difference between scientific laws and everyday experience?

Answer: Different quality, but same essence.

Scientific Causality Is Also Causal Impression

Richard Feynman said in his 1964 lecture:

“In general we look for a new law by the following process: First we guess it. Then we compute the consequences of the guess to see what would be implied if this law that we guessed is right. Then we compare the result of the computation to nature, with experiment or experience… If it disagrees with experiment it is wrong.”

Science isn’t “discovering objective truth,” but “high-frequency validated reason matching.”

Formation process of scientific causality:

Propose hypothesis (guess)
 ↓
Derive predictions
 ↓
Experimental verification (multiple times, multiple people, multiple conditions)
 ↓
If stable overlap → Form scientific causal impression
If unstable → Revise or overturn

Example: Law of Universal Gravitation

Newton didn’t “discover” universal gravitation but “guessed” a formula:

F = G × (m₁m₂)/r²

Then:

• Calculated what planetary orbits should look like
• Compared with observations: perfect match
• Scientists worldwide repeatedly verified: countless stable overlaps over 200 years
• Formed scientific causal impression: “Under macroscopic, low-speed, weak gravitational field conditions, this formula is effective”

Note the condition limitation:

• Not “absolutely effective”
• But “stable under specific conditions”

Characteristics of Scientific Causality

Not saying scientific causality is “absolutely correct,” but that it has “higher quality”

High quality of scientific causal impressions is reflected in:

1. Extremely high verification frequency

• Personal experience: A few to dozens of times
• Scientific laws: Millions of experiments

2. Extremely broad subjective overlap

• Personal experience: Individual or small group
• Scientific laws: Global scientific community consensus

3. High prediction accuracy

• Personal experience: Vague judgment
• Scientific laws: Quantitative predictions (can reach multiple decimal places)

4. Strong reproducibility

• Personal experience: Hard to repeat under different conditions
• Scientific laws: Anyone can repeat under same conditions

5. Active falsification

• Personal experience: Tends to reinforce existing impressions
• Scientific laws: Actively seek counterexamples

6. Clear applicable conditions

• Personal experience: Often omit conditions
• Scientific laws: Explicitly state applicable scope

Scientific Causality Is Still Condition-Dependent

Important recognition: Scientific causality is also not “absolute truth.”

Example: Newton vs Einstein

Newton’s causal impression:

Mass + Distance → Gravity (F=Gm₁m₂/r²)
Verification conditions: Macroscopic, low-speed, weak gravitational field
Effectiveness: Extremely stable for 200 years

Einstein’s revision:

Discovery: Mercury's perihelion precession anomaly (Newton couldn't explain)
New guess: Spacetime is curved
Verification: 1919 solar eclipse observation of light bending
Result: Under high-speed, strong gravitational field conditions, Einstein is more accurate

This isn’t “Newton was wrong”:

• Under macroscopic, low-speed conditions, Newton is still effective
• Einstein’s applicable scope is just broader
• This is rewriting of causal impression, not negation

This means:

• Scientific causality can also be revised
• There’s no “ultimate truth”
• Only “the highest quality reason under current conditions”

Characteristics of Everyday Causal Impressions

Compared to scientific causality: not essentially different, but different quality

Quality spectrum of causal impressions:

Hallucination < Superstition < Everyday experience < Scientific laws

Not categorically separate, but a continuous quality gradient

Characteristics of everyday causal impressions:

1. Low verification frequency

• A few to dozens of experiences

2. Narrow subjective overlap

• Individual or small group

3. Vague prediction

• “Probably like this”

4. Weak reproducibility

• May fail if conditions slightly different

5. Confirmation bias

• Tend to remember what came true, forget failures

6. Condition omission

• “Hard work leads to success” (omits resources, timing, luck…)

Comparison Table: Quality Difference, Not Essential Difference

Key Insights

1. Scientific causality is also subjective product

• Not “discovery of objective truth”
• But “highly stable consensus of academic community”

2. Scientific causality has extremely high quality

• But not “absolutely correct”
• But “optimal reason under current conditions”

3. Scientific causality can be revised

• Newton → Einstein
• Classical mechanics → Quantum mechanics
• Each revision expands applicable conditions, not total negation

4. Everyday causal impressions have value

• Though lower quality
• But necessary and effective in daily decisions
• Can’t be dismissed just because “not scientific”

5. Distinguishing “quality” isn’t “devaluing”

• Acknowledge science has higher quality
• Doesn’t equal devaluing everyday experience
• But clearly understanding their respective applicable scopes

Practical Wisdom

Toward scientific causality:

• Highly trust within its applicable scope
• But acknowledge condition dependency
• Stay open to future revisions

Toward everyday causal impressions:

• Use in everyday scenarios
• But beware of over-generalization
• Seek higher quality verification for important decisions

Mistakes not to make:

• ❌ Treat science as “absolute truth”
• ❌ Treat everyday experience as “all wrong”
• ❌ Demand scientific standards for all cognition

Correct attitude:

• ✓ Acknowledge quality differences
• ✓ Choose based on scenario
• ✓ Maintain updatability

Experience-Based Structure vs Knowledge-Based Structure

We just discussed causal impressions—how experience sediments into intuition. Now we need to distinguish a more important question:

Is the experience your personal experience, or told to you by others?

These two have fundamental differences.

Definition and Distinction

Experience-based Structure: Cognitive models formed through direct experience by the subject.

Characteristics:

• Conditioned reflex-like (quick invocation)
• Highly individualized (only effective for you)
• Easily partial (limited samples)

Knowledge-based Structure: Cognitive models formed through indirect learning (stories, narratives, education, cultural input) by the subject.

Characteristics:

• Shareable (can be transmitted to others)
• Risk of being misled (unverified)
• Needs transformation to become experience (detailed below)

Science: A special experience-based structure meeting reliability (reproducible verification) and falsifiability (allowing counterexamples to overturn).

Characteristics:

• More rigorous experience management method
• But still subjective product (consensus of scientific community)
• Highest quality experience-based structure

Fake Dog Refund Case Comparison

Let’s use the fake dog refund incident to understand these two structures’ differences.

My experience-based structure:

Legal origin → Highest weight → Rights protection success

This is my personal experience:
- Discovered sign loophole
- Held legal evidence
- Pressured official
- Finally got money back

This structure is "real" for me because I verified it.

Your knowledge-based structure:

If you only heard me tell this story:

For you, "legal origin is useful" is just knowledge.
You know "this happened," but you haven't personally verified.
This is knowledge-based structure.

Conditions for transformation:

If you encounter similar situation next time:
- Try using legal origin
- Hold key evidence
- Successfully protect rights

At this point, knowledge-based structure transforms into your experience-based structure.

Fundamental Differences Between the Two

Difference 1: Invocation Speed

Experience-based structure:

• Instant invocation (no need to think)
• Conditioned reflex-like
• Example: Seller sees complaint, immediately thinks “stonewall”

Knowledge-based structure:

• Needs active recall (“I remember a theory says…”)
• Needs scenario matching (“Does this apply?”)
• Example: You heard “rights protection needs finding legal loopholes,” but might not remember when things happen

Difference 2: Reliability

Experience-based structure:

• Reliable in similar scenarios
• But easy to over-generalize (limited samples)
• Example: Seller’s “stonewalling strategy” works 90% of time, but fails with me

Knowledge-based structure:

• Unverified, reliability unknown
• May be misled (story might be fabricated)
• Example: Success studies’ “hard work leads to success”

Difference 3: Shareability

Experience-based structure:

• Highly individualized
• Hard to completely transmit to others
• Example: My rights protection experience, you might not replicate even after hearing

Knowledge-based structure:

• Shareable, spreadable
• Easy to form culture, tradition
• Example: Concepts like “suffering is blessing” can pass through generations

Three Axioms

Axiom 1: Shareability Axiom

Knowledge-based structure is shareable and can be transmitted across individuals; experience-based structure is highly individualized and fully effective only for the experiencer.

Example:

• I tell you the fake dog refund method (knowledge transmission)
• But your situation might be different (different seller, evidence, official)
• So my experience can’t directly replicate to you

Axiom 2: Error Axiom

Experience-based structure is limited by finite samples, easily partial. Knowledge-based structure is limited by narrative transmission, easily misled. Science emphasizes verification but still isn’t absolute truth.

Example:

• Seller’s experience: “Stonewalling works” (partial, because he hasn’t met all types of customers)
• Knowledge: “Hard work leads to success” (misled, omits luck, resources and other conditions)
• Science: “Newtonian mechanics” (needs revision to relativity under high speeds)

Axiom 3: Embedding Axiom

Experience-based structure embeds more in intuition invocation stage; knowledge-based structure embeds more in rational reference stage.

Example:

• Seller sees complaint → Immediately stonewall (experience-based, intuitive)
• I think about strategy → Remember “legal origin has highest weight” (knowledge-based, rational reference)

Three Theorems

Theorem 1: Transformation Theorem

Others’ experience-based structure → I can only first absorb as knowledge-based structure. Through self-trial-and-error and verification → Then transform into my experience-based structure. When my experience is stable enough, generalizable → Can be compressed back to knowledge-based structure, entering the shared library.

Transformation path:

Others' experience → My knowledge → My verification → My experience → New knowledge (transmit to others)

Fake dog refund case:

My experience (rights protection success)
  ↓
I tell you → Your knowledge ("legal origin useful")
  ↓
You try yourself → Your verification
  ↓
Success → Your experience
  ↓
You write article → New knowledge (spread to more people)

Theorem 2: Science Formula Theorem

Science = Experience-based structure × (Reliability ∧ Falsifiability). Science isn’t truth transcending experience, but more rigorous experience management process.

Explanation:

• Science is also experience (experiments, observations)
• But requires reliability (reproducible verification)
• Also requires falsifiability (allows counterexamples to overturn)
• This makes science “highest quality experience-based structure”

Theorem 3: Partiality Necessity Theorem

Any individual’s experience is necessarily limited by finite samples, thus necessarily partial. Any knowledge obtained through narrative is necessarily compressed, thus necessarily distorted.

Corollary:

• No “completely accurate” experience exists
• No “completely reliable” knowledge exists
• Only countermeasure is: continuous revision

Case Analysis: Child Breaks Glass

Scenario Setting

A child kicks a ball at home and accidentally breaks glass. He faces a choice: admit or hide?

Case 1: Driven by Experience-Based Structure

Past experience:

This child broke things before and was severely criticized by parents, very painful.

Formed experience-based structure:
Damage → Admit → Get scolded → Pain

Current decision:

Sees broken glass → Immediately thinks "will get scolded"
  ↓
Conditioned reflex activation → Hide

This is experience-based structure at work: fast, intuitive, conditioned reflex.

Limitation:

• This experience might be partial (maybe parents won’t be so angry this time)
• But child has no time to verify, can only rely on past experience

Case 2: Driven by Knowledge-Based Structure

Knowledge input:

Child heard a story:
"Someone cleverly lied, avoided punishment, and was praised for being smart."

Formed knowledge-based structure:
Lie → Wisdom → Safety

Current decision:

Sees broken glass → Remembers that story
  ↓
Try to imitate → "The neighbor kid broke it"

Risk:

Parents investigate → Find flaws → Child is exposed
  ↓
Originally wanted to "avoid punishment" → Instead attracted heavier scolding

Structure rewrite:

Knowledge-based structure: "Lie = safe" (fails)
  ↓
New experience-based structure: "Lie = more serious consequences" (forms)

Case 3: Scientific Verification

Hypothesis:

"Honesty will be forgiven"

This is a knowledge-based structure (parents, teachers often say so)

Verification process:

1st time: Child honestly admits breaking cup → Parents criticize but attitude mild
2nd time: Child honestly admits staining clothes → Parents don't get angry
3rd time: Child honestly admits bad test score → Parents help analyze reasons
...
Nth time: Stable overlap

Form scientific experience:

"In my family, honesty usually gets relatively mild treatment"

This is high-quality experience-based structure:
- Multiple verifications (reliability)
- Clear conditions ("in my family")
- Falsifiable (if parents' attitude suddenly changes, will revise)

Comparison of Three Structures

Consequences of Multiple Reinforcements

Whether knowledge-based or experience-based structure, when used and reinforced multiple times, it evolves into deeper-level structures:

1. Methodology: Becomes problem-solving template after repeated invocation

• Example: “First lie to test reaction when things happen” (if lying works multiple times)
• Example: “Be honest first when things happen” (if honesty works multiple times)

2. Causal Impression: Forms “default causal chain,” deeply convinced

• Example: “Being beaten = growth” (if culture repeatedly emphasizes)
• Example: “Stonewall = customer gives up” (seller’s high-frequency experience)

3. Correlation Beliefs: Habitually associates unrelated information

• Example: “Wearing red = confident”
• Example: “Number 4 = unlucky”

These “structural sediments” ultimately determine a person’s patterns of processing information and responding to situations, constituting their “stable cognitive form.”

Transformation Mechanism: From Knowledge to Experience

Core process:

Hear about (knowledge-based)
  ↓
Try (verification)
  ↓
Feedback (result)
  ↓
Correction (adjustment)
  ↓
Stabilize (experience-based)

Fake dog refund transmission chain:

My experience: "Legal origin useful"
  ↓
I tell friend A: A gains knowledge
  ↓
A tries rights protection next time: A starts verification
  ↓
A succeeds/fails: A gets feedback
  ↓
After A tries multiple times: A's experience forms
  ↓
A tells friend B: B gains knowledge
  ↓
Loop...

Key points:

• Knowledge doesn’t automatically become experience
• Must go through “try-feedback-correct” cycle
• This cycle may need multiple iterations

Practical Wisdom

How to treat experience-based structure:

✓ Everyday scenarios: Trust your intuition (experience-based structure usually effective) ✓ Important decisions: Question your intuition (might be partial experience) ✓ New contexts: Beware over-generalization (your experience might not apply)

How to treat knowledge-based structure:

✓ Learning stage: Widely absorb (first as knowledge reserve) ✓ Application stage: Carefully verify (don’t blindly apply) ✓ Reflection stage: Question source (Who said? Why credible?)

How to achieve transformation:

✓ Actively try: Don’t just stay at “knowing” ✓ Observe feedback: Pay attention to whether actual results match expectations ✓ Timely correction: Adjust understanding if doesn’t match ✓ Multiple verifications: Don’t conclude from once

Traps to beware:

❌ Treat knowledge as experience (“I’ve read many books, so I understand”) ❌ Treat experience as truth (“My experience is right”) ❌ Refuse updates (“I’ve always done it this way”)

One-Sentence Summary

Experience-based structure gives us intuition, knowledge-based structure gives us reference, science is more reliable experience management tool; all three shape our cognition but carry partiality and limitations. The only countermeasure is: continuous verification, continuous revision.

Illusory Causality—Identification and Prevention

Now we know how causal impressions form and the difference between experience-based and knowledge-based structures. But there’s another problem:

Some causal impressions are wrong.

Not saying “not accurate enough,” but saying fundamentally illusory.

What Is Illusory Causality?

Illusory Causality = Causal impression abandons conditions due to compression, causing failure in new contexts

The key is: Conditions were omitted.

Under condition A, “X → Y” holds. But when you compress, you omit condition A. Then using it under condition B, it fails.

Illusion Type 1: Correlation Is Not Causation

Case: Ice Cream Sales and Drowning Accidents

Observed pattern:

Ice cream sales high ← → Drowning accidents many

Data shows these two indeed have correlation.

Wrong causal impression:

Eating ice cream → Causes drowning?

Real reason:

Hot weather → Ice cream sales high
Hot weather → People swim → Drowning accidents increase

Both are results of “hot weather” but no causal relationship between them.

Structural analysis:

• This is misreading of surface structure
• Two dimensions change simultaneously but no causal relationship
• There’s a common third factor (weather)

Prevention method:

• Ask: “Are there other possible explanations?”
• Look for hidden third factors
• Don’t easily treat correlation as causation

Illusion Type 2: Causality with Omitted Conditions

Case: Hard Work Leads to Success

Causal impression:

Hard work → Success

This is the most common chicken soup-style causality.

Omitted conditions:

• Resources (sufficient funds, connections, information)
• Timing (right thing at right time)
• Capability (basic abilities and learning ability)
• Environment (economic cycles, industry trends)
• Luck (uncontrollable random factors)

More accurate formula:

Hard work + Resources + Timing + Capability + Environment + Luck → Success (probabilistic)

Why omitted:

• Inevitable result of compression
• “Hard work” most easily observed and controlled
• Other factors too complex, automatically filtered

Harm:

• Successful people think “all due to my hard work” (ignore luck and resources)
• Failed people think “I’m not working hard enough” (ignore structural obstacles)
• Root of chicken soup literature

Prevention method:

• List omitted conditions
• Evaluate importance of each condition
• Acknowledge role of luck and environment

Illusion Type 3: Survivorship Bias

Case: Management Experience of Successful Companies

Observed pattern:

Successful companies → All have certain management method X

Wrong causal impression:

Adopt method X → Company success

Ignored information:

• Many failed companies also adopted method X
• But they’ve disappeared, we can’t see them

Real situation:

Companies adopting method X:
- Successful: 10 (widely reported)
- Failed: 100 (ignored)

Actual success rate: 10/110 ≈ 9%

Structural analysis:

• This is selective retention in compression chain
• “Successful” information amplified
• “Failed” information filtered

Prevention method:

• Ask: “What did failures do?”
• Look for counterexamples
• Beware narratives like “all successful people…”

Illusion Type 4: Hindsight Bias

Case: Predicting Stock Market

Post-event narrative:

Stock market rose
  ↓
Looking back
  ↓
"I knew it would rise"

Wrong causal impression:

My judgment → Stock market rose

Real situation:

• Beforehand there were countless possibilities
• Afterward only retained “came true” judgments
• Forgot “didn’t come true” judgments

Why this happens:

• Memory’s selective retention (compression)
• Brain automatically rationalizes past behavior
• Illusion of “I always knew”

Prevention method:

• Record predictions beforehand (timestamp)
• Honestly compare afterward
• Acknowledge contingency and uncertainty

How Causal Impressions Are Rewritten

In the fake dog refund incident, the seller’s causal impression was rewritten.

Old causal impression:

Customer complaint → Stonewall → 90% will give up

New experience:

Customer complaint → Stonewall → Met persistent one + Regulatory intervention → Forced refund

New causal impression (possibly):

Customer complaint → Stonewall → Sometimes meet persistent ones
If customer holds legal evidence → Stonewalling might fail

His success rate dropped from “90%” to “85%” or “80%”.

Causal impressions aren’t permanent; they’re revised by new experience.

Three Ways of Rewriting

Way 1: Counterexample Appears

You always thought “stones all sink,” one day see pumice floating on water.

Old causal impression revised:

Stone → Will sink
Revised to:
Most stones sink, but pumice is exception

Way 2: Leap Reconstruction

You always thought “overtime → promotion,” but one day discover coworker who overworked most was laid off.

Leap happens, entire causal chain reconstructed:

Old: Overtime → Good performance → Promotion
New: Overtime → Not necessarily good performance → Might be laid off (sign of low efficiency)

Way 3: Weight Adjustment

You have two causal impressions:

A: Hard work → Success (60%)
B: Luck → Success (40%)

Once you worked very hard but failed, while seeing others succeed with luck.

Weight adjustment:

A: Hard work → Success (50%)
B: Luck → Success (50%)

Manipulating Causality—Beware Engineered Inevitability

Essence of Manipulation: Creating Causal Impression Links

All manipulation is creating high-frequency causal impression links.

Advertising manipulation:

Use our product → Happiness

Mechanism:

• Through massive repetition (ad placement)
• Through carefully designed images (beautiful scenarios)
• Building causal impression in your brain

Why effective:

• After seeing 100 times, brain automatically forms link
• Even if you “know this is advertising”
• Causal impression still forms

Case 1: Consumerism’s Causal Impression

Implanted links:

Purchase → Happiness
New model → Status
Brand name → Success

Mechanism:

• Countless ads, films, social media
• High-frequency repetition of same images and narratives
• Forms consensus in collective unconscious

Result:

• You “naturally” want to purchase
• You “instinctively” pursue new models
• You “take for granted” brand names represent success

But whose intention is this:

• Not your original intention
• But intention implanted by commercial system

Case 2: Political Propaganda’s Causal Impression

Implanted links:

Us → Justice
Them → Evil
Obedience → Safety
Questioning → Danger

Mechanism:

• Control information compression method (only show favorable)
• High-frequency repetition of same narrative
• Suppress different voices

Result:

• Forms “us vs them” binary thinking
• “Naturally” support one side
• “Take for granted” reject other side

Case 3: Success Studies’ Causal Impression

Implanted links:

Attend courses → Success
Buy books → Change
Join training → Breakthrough

Mechanism:

• Selectively display success cases (survivorship bias)
• Omit failure cases and conditions
• Create illusion of “as long as you’re willing”

Result:

• People continuously purchase courses
• Attribute failure to “not working hard enough”
• Continue purchasing next course

Two Methods to Identify Manipulation

Method 1: Question Compression Process

When someone shows you a causal link:

• What information was omitted?
• How many counterexamples are hidden?
• What premises is this link built on?

Method 2: Find Where Interest Lies

• Who’s promoting this causal link?
• What can they gain from it?
• Who benefits if I adopt this link?

Example:

“Join our training course → You’ll succeed”

• Method 1: What was omitted? (Other conditions needed for success: capability, resources, luck)
• Method 2: Who profits? (Training company makes money, you pay money)

Combining both methods can identify most manipulation.

Summary

How does experience form? Through three levels:

Level 1: Causal Impression

• Compression of high-frequency combinations
• From “this time +0.6” to “this shop → good”
• Forms quickly invokable intuition

Level 2: Experience-Based and Knowledge-Based Structures

• Experience-based: Personal experience, conditioned reflex-like
• Knowledge-based: Indirect learning, needs verification
• Science: High-quality experience-based structure

Level 3: Quality Management

• Identify illusory causality (correlation ≠ causation, omitted conditions, survivorship bias, hindsight)
• Prevent manipulation (question compression, find interests)
• Continuous revision (causal impressions can be rewritten)

Core insights:

• Causal impression isn’t “discovery of objective laws” but “compression of experience”
• Science is also causal impression, just higher quality
• Experience-based structure ≠ knowledge-based structure, transformation needs verification
• All causal impressions are condition-dependent, no absolute truth
• Beware implanted causal links

Practical value:

• Everyday scenarios: Trust experience, quick invocation
• Important decisions: Question experience, look for counterexamples
• Learning stage: Absorb knowledge, prepare verification
• Application stage: Carefully apply, observe feedback
• High manipulation risk scenarios: Question compression process

Ultimate goal:

See contingency in “inevitability,” see possibility in “taken for granted.”

Recognize experience’s value, also recognize experience’s limitations.

But there’s an even deeper question:

Where does intention itself come from?

We say “intention determines manifestation,” say “intention determines origin,” but intention itself?

• Why did I generate the intention to “recover cheated money”?
• Why did the seller generate the intention to “stonewall and delay”?
• Why did the official’s intention instantly change after hearing “have something to tell the mayor”?

Causal impression explains “why there’s such intuition.”

But intention is deeper—it determines what kind of causal impressions you’ll form.

A person who cares about health accumulates “food → health impact” causal impressions.

A person who cares about efficiency accumulates “method → efficiency improvement” causal impressions.

It’s intention that screens your experience and shapes your causal impressions.

Next Chapter: Where Intention Comes From