How Instruction-Waiting Workers Can Survive the AI Era: The Thought-Delegation Trap and Escape Routes

• Target audience: People who often freeze with “I don’t know what to do”; managers struggling with instruction-waiting subordinates; people training Generation Z workers; those worried about colleagues or family members stuck in this pattern
• Prerequisites: None
• Reading time: 13 minutes

Overview

This is the most realistic, hardest, and most important article in this AI-era career guide series. It covers what may be the dominant working style in Japan’s labor market — Gallup’s 2024 survey showed that Japanese employee engagement is only 6%, the lowest in the world, while 24% are actively disengaged, making the ratio 4 to 1¹ — a pattern called the instruction-waiting type.

“Instruction-waiting” is not a personality but a state. Most of the time, it isn’t laziness; it’s closer to learned helplessness produced by environment and education. During Japan’s high-growth era of manufacturing and through the 2000s era of waterfall-style system integration, this state was in fact a rewarded way of working. There was a time when people who executed instructions precisely created significant value.

But AI is quietly ending that game. This article describes the three-stage shift the instruction-waiting type faces in the AI era (short-term booster → deepening dependence → replacement), and offers realistic escape routes. Read it as an invitation to recognition, not judgment. Not condemnation, but awareness.

For the relationship to the other two types (spec-driven and explorer) and the psychological background of this article, see The Psychology of People Who Only Want Clean Work.

30-Second Self-Diagnosis

If three or more of these ring true, this article is for you:

1. □ When given a new task, you feel “I want someone to tell me what to do”
2. □ You can’t confidently judge the quality of your own output
3. □ You frequently want to ask “is this right?” to your manager or colleagues
4. □ When things fail, you feel “I was just doing what I was told” (or have felt this)
5. □ When asked “what’s your opinion?” you often don’t know what to say

If three or more match, this article is for you. Read to the end.

If zero to two match, you probably lean toward spec-driven or exploration-driven type. Proceed to those playbooks.

Instruction-Waiting Is Not a Personality — It’s a State

I want to start with the most important framing.

When you hear “instruction-waiting type,” you might imagine “lazy, irresponsible people.” That’s a misreading. In psychological terms, this state is almost always produced by the environment. You’re in this mode not because of your personality but because you’ve lived in an environment where this mode functioned as a survival strategy.

Learned Helplessness as a Mechanism

Learned helplessness, discovered by Martin Seligman in the 1960s, describes how animals and humans placed in environments where “nothing I do changes the outcome” eventually stop trying at all². The key point: this state is not “laziness” — it’s an adaptive strategy the brain has learned.

Recent neuroscience has shown that the brain under stress defaults to passivity as an unlearned response². “Do nothing; it’s safer” becomes the default neural pathway. This is not individual weakness — it’s the basic design of the human brain.

Most of the instruction-waiting behavior observed in Japanese workplaces is probably a mild variation of this learned helplessness pattern.

• Micromanagement environments: Repeated experiences of being overridden teach “my judgment gets rewritten anyway”
• Lack of psychological safety: Experiences of being punished for speaking up build circuits that suppress initiative
• Strict failure culture: “Don’t try” becomes safer than “try and fail”
• Correct-answer education: School trains you to “pick the right answer” — you rarely practice making decisions yourself

In other words, instruction-waiting is adaptation to environment, not personal choice. Without this recognition, the rest of the article won’t land correctly.

Historically, This Was a Rewarded Way of Working

Further, this state was, historically, actually rewarded.

• Japan’s high-growth-era manufacturing: Precisely repeating clear procedures was the source of international competitiveness
• Early-2000s SI industry: Writing code according to detailed specifications was the core revenue model
• Bureaucracies and large corporations: Rule-following and procedural accuracy were the evaluation criteria

Many people who worked in instruction-waiting mode were correctly rewarded within the environment that shaped them. They weren’t “wrong.” The rules changed.

When Education Doesn’t Develop Self-Direction — The Gen Z Case

The two paths above (learned helplessness, historically rewarded) both describe states formed in adulthood by environmental pressures. They describe people who could have been self-directed, but whose capacity was put into dormancy.

But there’s a structurally different third path: when the capacity for self-direction itself was not built during formative years (childhood through adolescence). This is particularly visible in Generation Z (roughly 1997–2012 births) and later³.

Loss of Autonomy and Free Play

Social psychologist Jonathan Haidt argued in his 2024 book The Anxious Generation that the “deprivation of children’s autonomy” that happened across the English-speaking world since the 1990s has produced today’s epidemic of anxious, dependent youth³.

“We have vastly overprotected our kids in the real world…”

Parents began to overprotect children in the “real world” — reducing free play to avoid risk, keeping them under constant adult supervision. As a result, children lost the cumulative experience of judging for themselves, trying, failing, and recovering. At the same time, they were handed smartphones — a device that “always gives you the right answer.” Haidt calls this combination “overprotection in the real world and underprotection in the virtual world,” an environment that fundamentally damages the development of self-determination and self-efficacy.

Note: Haidt’s thesis is widely discussed but also academically contested, particularly around correlation-versus-causation issues. Researchers like Candice Odgers (Nature, 2024) and Amy Orben have pointed out that the evidence for a causal link between smartphones and mental health is methodologically weaker than Haidt suggests. However, this article uses Haidt’s framework only for the argument about “reduced opportunities for autonomy development,” not for the mental-health causation claim. This particular structural observation is broadly accepted even by Haidt’s critics³.

Correct-Answer Education as Internalization

Japan’s educational environment has, beyond the Western overprotection issue, an even older structural problem: correct-answer-focused education.

The evaluation criterion for exams is the ability to select predetermined correct answers. The ability to pose questions and make judgments yourself isn’t assessed — worse, original answers can lose points. For 12 to 18 years, children learn experientially that “it’s more rewarded to memorize answers than to judge for yourself.”

This isn’t individual laziness. It’s the result of a system in which the evaluation structure punishes self-direction. It’s completely rational adaptation from the individual’s perspective, but the cumulative experience of crossing the self-direction threshold never happens.

Digital Natives and the “Answer Externalization” Habit

Gen Z’s other defining feature is being smartphone-native. From birth, “if I don’t know something, I ask the device immediately” has been habituated from formative years. The process of thinking to arrive at an answer has always competed with the “just search for it” option.

This has decisive meaning in the AI era. For Gen Z, AI isn’t a new tool — it’s an extension of the existing “get the answer from outside” habit. Where previous generations ask themselves “wait, isn’t asking AI just outsourcing thinking?”, Gen Z doesn’t even have the prompt to ask that question. The thought-delegation trap is, for them, not a trap but an internalized default state.

Failure Avoidance and the Ritualization of Job Hunting

Japan’s Gen Z, raised during prolonged economic stagnation and watching their parents’ “Lost Generation” struggles up close, developed a strategy of prioritizing failure avoidance over challenge as rational adaptation. This isn’t just “conservative” — it’s adaptation to the social environment they were placed in.

A Japan-specific factor: the ritualization of job hunting (shukatsu). The final big decision of student life — job searching — has become an experience not of “choosing by your own judgment” but of “filling in a form according to a standard template.” Combined with mass new-graduate hiring structures, the last opportunity to cross the self-direction threshold can be structurally lost.

Why This Path Is the Hardest to Intervene In

This third path (c) is essentially different from the first two. The first two (learned helplessness and historically rewarded) describe states where “capacity existed and was later suppressed.” The third (c) describes a state where “the capacity itself was never built.”

Path	State	Intervention method
(a) Historically rewarded	Ability is dormant	Change environment to reactivate
(b) Learned helplessness	Ability is suppressed	Small successes to recover
(c) Education didn’t develop it	Ability itself is unbuilt	Accumulate experience from zero

There’s nothing to reactivate. It has to be built from scratch. “Experiencing for the first time” is the required approach. The “First Step” section later in this article addresses this — path (c) people need smaller, more graduated entry points.

But There’s Hope — Threshold Crossing Can Happen at Any Age

I don’t want to end this section on a dark note, so let me emphasize: even on path (c), the self-direction threshold can be crossed. Being a late starter doesn’t make it impossible.

Haidt himself proposes multiple interventions for restoring children’s autonomy³, but the same principles work in adulthood: accumulate experiences of deciding, trying, and learning from failure, little by little. For a 5-year-old, it’s removing training wheels. For a 20-something, it’s making small work choices yourself. For a 40-something, it’s deciding side projects or career changes by your own criteria. Different entry points, same substance.

And the fact that you’re reading this article to the end means you’ve already taken the first step of recognition — the beginning of threshold crossing. Recognition alone isn’t enough, but threshold crossing doesn’t happen without it.

The Self-Direction Threshold — Who’s Crossed It, Who Hasn’t

Return to the 30-second diagnostic at the start. That diagnostic measures current behavioral patterns. But there’s a second dimension that behavioral patterns alone miss: whether you’ve ever crossed the threshold of self-direction in your past — a developmental axis.

What Does Crossing the Threshold Mean?

“Crossing the self-direction threshold” refers to a state where the accumulated experience of judging by your own standards without being asked, acting, and taking responsibility has solidified into an internal mode of “I decide and move.” This is a widely observed developmental phenomenon in education and psychology, related to Dreyfus’s skill acquisition model, Vygotsky’s Zone of Proximal Development, and Self-Determination Theory’s concept of autonomy.

Crucially, threshold crossing is independent of career stage. Some people cross it in their student years; some never cross it in their 40s. A 22-year-old new hire can be self-directed; a 30-year veteran can still be instruction-waiting. Age and years of experience are extremely weak predictors of self-direction.

Four Trajectory Patterns

At least four patterns of self-direction threshold crossing are observable in the labor market:

Pattern	When threshold is crossed	State at labor market entry	Features
Early-autonomy type	Student years (middle/high school to college)	Already spec-driven or exploration-driven	Voluntary project experience, healthy questioning of authority
Mid-career awakening type	Mid-career (30s–50s)	Starts instruction-waiting → later transitions	Job change, project failure, AI era etc. as triggers
Chronic stasis type	Never crossed	Lifetime instruction-waiting	Environment consistently punished self-direction
Regression type (rare)	Crossed once, regressed	Spec/exploration → instruction-waiting	Excessive micromanagement caused reversion

Important note: Among these four patterns, the share of the “early-autonomy type” may be historically shrinking with Gen Z and later. The (c) path factors discussed earlier — overprotection, correct-answer education, smartphone-native habits — are structurally reducing the opportunities to cross the threshold during student years³. The assumption that “a certain portion of new hires are already self-directed” is becoming shaky. This isn’t “Gen Z is inferior” — it’s “the environment is taking away opportunities for autonomy development.”

Early-autonomy-type people don’t need to read this article. You’ve already crossed the threshold, and even if you’re in “I don’t know what to do” mode now, it’s a knowledge gap, not a self-direction issue. Go to the spec-driven playbook or the explorer playbook.

Supplementary Diagnostic — Measuring Threshold Crossing from Past Experience

To determine which pattern you fit, here’s a past-experience-based supplementary diagnostic, a different axis from the initial (current behavior) diagnostic.

Count how many of these five experiences apply to you.

1. □ You’ve created, researched, or written something on your own interest — continuously, not just once — without anyone asking
2. □ You’ve experienced “this is wrong” and pushed back against authority or convention (teachers, parents, bosses, textbooks, industry wisdom)
3. □ You’ve chosen major life decisions (school, job, where to live, partner) by your own standards, not others’ expectations
4. □ When you’ve failed, you’ve analyzed the cause yourself before asking for help
5. □ You can explain your strong likes/dislikes with reasons (technology, work, creative output, relationships)

If three or more match: You’ve likely already crossed the self-direction threshold. Even if the initial diagnostic flagged you as instruction-waiting, it’s probably a temporary state (knowledge gap in a new domain, temporary environmental pressure, fatigue or anxiety effects). The “three stages” and “escape routes” in the rest of this article don’t directly apply to you. Instead, read the spec-driven playbook or the explorer playbook based on your core traits.

If zero to two match: Self-direction threshold crossing may not have happened yet. This doesn’t mean you’re inferior. Threshold crossing is a developmental phenomenon that can happen at any age, and whether it happens depends more on cumulative experience and triggers than on your abilities. The rest of this article is the material for building that accumulation.

Threshold Crossing Can Happen at Any Age

The most important message, before returning to the main thread:

The self-direction threshold can be crossed at 15 or 50. This isn’t a fixed innate trait — it’s a developmental phenomenon that can happen to anyone given cumulative experience and the right triggers. Late starters aren’t inferior to early starters. They just had their trigger arrive later.

And the current AI-era situation offers unprecedented numbers of threshold-crossing triggers. AI provides instant answers, which on the surface delays threshold crossing. But flip it around: the micro habit of “think before asking AI” becomes threshold-crossing training. The “First Step” section later designs exactly this kind of micro habit.

Back to the main thread. If you tested as instruction-waiting both in the initial diagnostic and the supplementary one (0–2 matches), you’re exactly the central reader for what follows.

The Three-Stage Shift AI Brings

Here’s the main content. AI affects the instruction-waiting type with stage-specific forces. Miss this, and you’ll be captivated by short-term benefits while blind to the long-term crisis.

flowchart TB
    Start["Instruction-Waiting (Present)"]
    Start --> S1["Stage 1: Short-term booster<br>(now to ~2027)<br>AI thinks for you<br>Productivity spikes, looks like winning"]
    S1 --> S2["Stage 2: Deepening dependence<br>(~2027-2030)<br>Can't move without AI<br>Internal standards disappear"]
    S2 --> S3["Stage 3: Replacement<br>(post-2030)<br>AI completes tasks unsupervised<br>Employment value vanishes"]

    classDef boost stroke:#d29922,stroke-width:2px
    classDef warning stroke:#cf222e,stroke-width:3px
    class S1 boost
    class S2,S3 warning

Stage 1: Short-Term Booster (Now to ~2027)

For the first two or three years, instruction-waiting people are the biggest beneficiaries of AI.

Brynjolfsson, Li, and Raymond’s large-scale 2023 study (NBER working paper, published in Quarterly Journal of Economics 2025) analyzed the rollout of a generative AI assistant in a customer support operation and found that novice/low-skilled workers showed 34% productivity improvements⁴, while experienced/high-skilled workers showed almost no change. AI delivers its biggest benefits to the people who previously paid the highest cost for thinking.

The mechanism is obvious. Ask AI “what should I do about this?” and get a plausible answer. Without thinking, produce productive-looking output. People who used to constantly ask their manager now “self-propel” with AI’s help. Metrics rise.

Most people here experience the illusion of winning.

Stage 2: Deepening Dependence (~2027–2030)

But a quiet shift progresses in the next stage.

As you get used to letting AI think, you don’t develop the ability to evaluate for yourself. Cacioppo & Petty’s 1982 research on Need for Cognition⁵ showed that people who avoid thinking gradually lose the capacity to think. This is the “cognitive miser” trap.

Concretely, what happens:

• When AI produces “averagely good” output, you accept it as-is
• The “this is wrong” sensation stops arising
• The ability to choose among multiple options atrophies
• The habit of having your own opinions fades
• The internal standards for doubting AI output disappear

What’s lost in this phase is taste. And once taste is lost, it doesn’t return without deliberate retraining. Your internal standards get pulled toward “averagely plausible AI output” and homogenize.

From the outside, you still look productive. AI helps you produce output. But you’re approaching a state where you can make no judgments without AI.

Stage 3: Replacement (Post-2030)

In the final stage, a structural shift occurs.

AI agent technology matures, and humans are no longer needed to supervise complex tasks. Even today, Claude, GPT, Gemini, and the other major AIs are expanding domains where they complete everything from implementation planning to execution given a rough description of intent.

When this happens, the value of the role “give instructions to AI and execute” collapses rapidly. Because AI itself can take on that role. The value instruction-waiting people provided — “execute what you’re told precisely” — can be met by AI alone.

Two kinds of employment remain:

1. The judge role: Selecting “this one is correct” from multiple AI outputs. Requires spec-driven strong internal standards.
2. The explorer role: Defining new problems AI hasn’t seen, setting direction. Requires exploration-driven parallel-holding capacity.

The “executor role” that instruction-waiting people filled doesn’t fit either.

This isn’t a slow change over decades. Given the capability improvement pace of Anthropic, OpenAI, Google, and others, Stage 3 will likely arrive well before today’s new graduates reach retirement.

Why This Happens — The Fundamental Thought-Delegation Trap

One structural issue underlies all three stages.

When you externalize thinking, the internal standards for “what is right” also disappear — this is the biggest trap of thought delegation.

This problem predated AI. Someone who relies entirely on GPS can’t remember city geography; someone who relies entirely on calculators loses mental math. In psychology and neuroscience, this is called cognitive offloading. Offloading itself isn’t bad — humanity has always used tools to reduce cognitive load.

But AI offloading has a feature no previous tool had: you can offload “judgment itself.” GPS remembers the roads but doesn’t decide “where you want to go.” Calculators compute but don’t tell you “what to calculate.”

But ask AI “how should I do this?” and it returns what you should do, including the judgment. You can externalize all the way up to judgment. This is what’s fundamentally different from past cognitive offloading.

Keep externalizing judgment, and what you want, what you like, what feels off — the internal voices — gradually become inaudible. This is the loss of taste, the atrophy of agency, and in the long term, the disappearance of professional identity.

Escape Routes — Which Direction to Walk

From here, practical matters.

Escape from instruction-waiting means transitioning to either spec-driven or exploration-driven. They’re opposite in traits, but both share “being a thinking subject.” The first step to becoming a thinking subject is similar whichever route you take.

Route 1: Transition to Spec-Driven

Start from training to verbalize your “likes and dislikes.”

• Write one “this is good” and “this is bad” every day: Code, design, writing — anything. Training the act of judgment
• Practice saying “this is wrong” to AI output: Start with just the feeling. Gradually it becomes words
• Pick one “non-negotiable” for each deliverable: Just one is enough. Make it the starting point of your standards
• Make one decision a day without seeking approval: Ban “is this right?”

Details: The Spec-Driven Playbook.

Route 2: Transition to Exploration-Driven

Start from training to “just get it running.”

• Move your hands on imperfect state: Progress one step even without the answer
• Assume failure: Start with throwaway prototypes. Not success or failure, but “exploration”
• Say “I don’t know” out loud: Verbalize the unknown state. Not asking — recognizing
• Try three different approaches to the same problem: Training parallel holding of options

Details: The Explorer’s Playbook.

Which to Choose?

It depends on which trait you originally had. Most instruction-waiting people have their original trait suppressed by environment. Use these questions to explore your original nature.

• In childhood or school years, was “I like this / dislike this” a strong feeling? → Spec-driven aptitude
• Was there a period when “just try it” came naturally? → Exploration-driven aptitude
• If neither, start from either route. Do it and deepen whichever fits

First Step — What You Can Do Tomorrow

As a realistic first step, here are three concrete actions you can start today or tomorrow.

1. Block “Is This Right?” Once

Next time you’re given a task, instead of asking “is this right?” as usual, judge by yourself and submit — just this once. It’s fine if you’re wrong. Learning from mistakes is the raw material for the next judgment.

2. Think for 3 Seconds Before Asking AI

Before asking AI, ask yourself “what do I think?” for just three seconds. The answer can be “I don’t know.” Then ask AI, and compare AI’s answer to your initial reaction. This trains taste preservation.

3. Write Down One “Dislike” a Day

At the end of the day, write down one thing from what you saw, read, or wrote that day that you “disliked” on paper or in a note. No need for reasons. “Somehow disliked” is fine. This excavates your buried internal standards.

All three take under 5 minutes to start and need no special training or environment. Recovery of small agency is the starting point of long-term escape.

For Path (c) — Even Smaller Entry Points

If the three actions above feel too hard — especially for Gen Z on path (c) (where education didn’t develop self-direction) — you need even smaller entry points.

For path (c), “making a judgment” is too heavy both cognitively and emotionally. You need to start from training the “internal sensations that come before judgment.” These three are one step earlier than the three above. No evaluation or judgment required. Just practice feeling your own inside.

a. Find Five “Likes” Over a Week

Over one week, find and note down five things you felt “I like this.” Not judgment or evaluation. Just confirming that the most primitive internal response — liking something — exists within you. Code style, food, music, anything. You don’t have to write why. Just capture the moments when you felt liking.

The meaning of this training: discovering that there exists “a response independent of others’ evaluation” inside you. Path (c) people often filter every reaction through “is this right?” and erase pure liking before feeling it. Start with feeling.

b. Make One Prototype No One Will See

Make something on the premise that no one will see it. Code, writing, drawing, cooking — any genre. The condition “no fear of evaluation” is decisive. People raised in environments of constant surveillance and evaluation sometimes can only experiment freely under “no one sees” conditions.

The work doesn’t have to be finished. No questions needed. No answers needed. Just one experience of moving your hands without anyone’s eyes on you. This is the first sample of the “moving for yourself” sensation.

c. Think “This Is Wrong” Once a Day, Silently

No need to say it out loud. No need to tell anyone. Just, at some point during the day, think “this is wrong” silently, about something you saw, read, or heard. The object of dissent can be trivial. A boss’s comment, a textbook passage, an AI response, someone’s social media post.

The meaning: developing the internal sensation of dissent. Path (c) people often carry an unconscious feeling that “having opinions at all is forbidden.” First, accumulate the experience of dissenting in a safe place where no one knows — silently, in your head. This becomes the foundation for having opinions later.

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Unlike the three actions for paths (a) and (b), these three for path (c) don’t produce immediate productivity gains. They train “the stage before judgment” and take time to show effects. But if you skip this stage and jump to “block ‘is this right?’”, most people face anxiety and confusion and fail.

No need to rush. For path (c), stepping one step per week is enough. Small likes → unseen prototypes → silent dissent → outer judgment — walk through this order step by step. This accumulation eventually connects to the larger change of crossing the self-direction threshold.

Summary — Recognition Is the First Step

Instruction-waiting is not a personality but a state. Most of it is something close to environment-produced learned helplessness. You’re in this state not because of laziness or lack of ability but because you spent too long in an environment where this way of being was rewarded.

But AI is quietly ending that game. Short-term benefit, medium-term dependence deepening, long-term role replacement — this three-stage shift is already in Stage 1.

Reading this article to the end means you’ve already taken the first step of recognition. Recognition is a uniquely human action that AI can’t replace. In an era when AI gives instructions, the very act of asking “what state am I in?” is itself the first sign of leaving the instruction-waiting state.

The next step is to proceed to Playbook A or B. Either is fine. What matters is that you make the choice.

Related Articles

Playbooks (Escape Route Details)

• The Spec-Driven Engineer’s AI-Era Playbook — Tactics for transitioning to spec-driven
• The Explorer’s AI-Era Playbook — Tactics for transitioning to exploration-driven

Background Psychology and AI-Era Thought

• The Psychology of People Who Only Want Clean Work — Theoretical background on Tolerance for Ambiguity, Need for Cognitive Closure, and the three-type classification
• Why Process-Attached People Struggle With AI — Process vs. outcome motivation structures
• The Psychology of Perfectionism — Two faces of adaptive/maladaptive perfectionism
• Generator-Verifier Pattern: Why LLMs Work Better with “Find” Than “Don’t” — Why evaluation is fundamentally cheaper than generation

References

1. Only 6% of Japanese Workforce is Engaged, Among The Lowest in the World - Gallup, State of the Global Workplace 2024 Report. Japan engagement rate 6%, lowest worldwide. Actively disengaged 24%, ratio 4:1. 【Reliability: Medium-High】 ↩︎

2. Learned Helplessness at Fifty: Insights from Neuroscience - Maier, S. F., & Seligman, M. E. P. (2016). Psychological Review, 123(4), 349-367. Neuroscientific update of learned helplessness. Argues that passivity under stress is the brain’s default state. 【Reliability: High】 ↩︎ ↩︎²

3. The Anxious Generation: How the Great Rewiring of Childhood Is Causing an Epidemic of Mental Illness - Haidt, J. (2024). Penguin Press. Argues that the autonomy deprivation and smartphone-native environment since the 1990s has produced Gen Z anxiety, dependence, and reduced self-efficacy. The central contrast is “overprotection in the real world and underprotection in the virtual world.” Widely discussed via reviews in NPR, The Washington Post, and elsewhere. However, a critical Nature review by Candice Odgers (2024) and methodological criticism by Amy Orben and others also exist; academic debate continues. This article references Haidt only for the “reduced opportunities for autonomy development” structural observation, not the mental-health causation claim. 【Reliability: Medium-High (contested topic)】 ↩︎ ↩︎² ↩︎³ ↩︎⁴ ↩︎⁵

4. Generative AI at Work - Brynjolfsson, E., Li, D., & Raymond, L. (2025). Quarterly Journal of Economics, 140(2), 889-942. Study of 5,172 customer support agents. 15% overall productivity improvement, 34% improvement for novice/low-skilled workers, minimal change for experienced workers. Originally published as an NBER working paper in 2023. 【Reliability: High】 ↩︎

5. The Need for Cognition - Cacioppo, J. T., & Petty, R. E. (1982). Journal of Personality and Social Psychology, 42(1), 116-131. Initial paper on Need for Cognition. 34-item scale measuring individual differences in intrinsic tendency toward effortful thinking. 【Reliability: High】 ↩︎