docs(10_Wiki): 위키 전체 재구성 — Topic_* 폴더를 4개 카테고리로 통합 + 대규모 중복 제거
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Antigravity Agent
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---
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id: wiki-2026-0508-cognitive-evaluation-theory
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title: Cognitive Evaluation Theory (Self-Determination)
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [self-determination theory, SDT, autonomy, competence, relatedness, intrinsic motivation, over-justification]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.88
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verification_status: applied
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tags: [psychology, motivation, sdt, intrinsic-motivation, gamification, game-design, education, productivity]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: psychology
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applicable_to: [Game Design, Education, Workplace, Product UX]
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---
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# Cognitive Evaluation Theory / SDT
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## 매 한 줄
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> **"매 reward 의 sometimes 의 passion 의 kill"**. Deci & Ryan 의 SDT (Self-Determination Theory) 의 sub-theory. 매 autonomy + competence + relatedness 의 3 의 intrinsic motivation 의 fuel. 매 modern gamification, 매 education, 매 product UX 의 base.
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## 매 핵심
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### Self-Determination Theory 의 3 needs
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1. **Autonomy**: 매 self-chosen.
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2. **Competence**: 매 mastery feel.
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3. **Relatedness**: 매 connection.
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→ 매 3 의 satisfy = 매 intrinsic motivation.
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### Intrinsic vs Extrinsic
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- **Intrinsic**: 매 enjoyment / interest 의 itself.
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- **Extrinsic**: 매 reward / punishment.
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- **Internalization**: 매 extrinsic 의 internalize 의 spectrum.
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### Over-justification effect (Lepper)
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- 매 paid for activity 의 enjoy → 매 intrinsic 의 lose.
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- 매 famous: 매 kid drawing experiment.
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- 매 implication: 매 reward 의 careful design.
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### 매 reward 의 effect
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| Type | Effect |
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|---|---|
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| Tangible + expected | 매 intrinsic ↓ |
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| Verbal positive | 매 intrinsic ↑ |
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| Unexpected | 매 less harmful |
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| Task-contingent | 매 intrinsic ↓ |
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| Performance-contingent (informative) | 매 mid |
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| Choice-supportive | 매 intrinsic ↑ |
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### 매 application
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#### Game design
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- **Autonomy**: 매 quest choice, 매 customization.
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- **Competence**: 매 progression, 매 skill curve.
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- **Relatedness**: 매 multiplayer, 매 guild.
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#### Education
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- **Autonomy**: 매 project topic choice.
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- **Competence**: 매 scaffolded difficulty.
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- **Relatedness**: 매 peer collab.
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#### Workplace
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- **Autonomy**: 매 schedule, 매 method.
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- **Competence**: 매 challenging task + feedback.
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- **Relatedness**: 매 team belonging.
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#### Product UX
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- **Autonomy**: 매 customization, 매 control.
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- **Competence**: 매 onboarding 의 mastery.
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- **Relatedness**: 매 social feature.
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### 매 dark side (anti-pattern)
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- 매 tangible reward 의 dominate (cash, gift card).
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- 매 surveillance / monitoring → autonomy ↓.
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- 매 forced ranking → relatedness ↓.
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- 매 over-difficult / under-difficult → competence ↓.
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### 매 modern AI 의 의미
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- **AI assistant**: 매 user 의 competence 의 reduce 의 risk.
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- **Recommendation**: 매 autonomy 의 illusion vs reality.
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- **Gamification**: 매 manipulation 의 risk (dark pattern).
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- **Productivity tracker**: 매 surveillance.
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## 💻 패턴 (응용)
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### Game progression (autonomy + competence)
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```ts
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class QuestSystem {
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// 매 autonomy: 매 player 의 choose
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getAvailableQuests(player) {
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return this.quests.filter(q =>
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q.unlock(player) &&
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!q.completed(player)
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); // 매 multiple option
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}
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// 매 competence: 매 skill curve
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recommendNextQuest(player) {
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const available = this.getAvailableQuests(player);
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const skillLevel = player.estimatedSkill();
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// 매 zone of proximal development
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return available
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.filter(q => q.difficulty >= skillLevel - 1 && q.difficulty <= skillLevel + 2)
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.sort((a, b) => a.difficulty - b.difficulty)[0];
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}
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}
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```
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### Workplace autonomy (4-day workweek-style)
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```python
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def autonomy_audit(team):
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return {
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'schedule_flexibility': measure(team, 'self-set start/end times'),
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'method_flexibility': measure(team, 'choose how to solve'),
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'topic_flexibility': measure(team, 'pick what to work on'),
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'tool_flexibility': measure(team, 'choose tools'),
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'location_flexibility': measure(team, 'remote / hybrid'),
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}
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```
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### Educational scaffolding (competence)
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```python
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def adaptive_difficulty(student, current_skill, performance):
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"""매 zone of proximal development."""
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if performance > 0.85:
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return current_skill + 1 # 매 challenge ↑
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elif performance < 0.5:
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return current_skill - 1 # 매 ease
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return current_skill # 매 stable
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```
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### Avoid over-justification (verbal > tangible)
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```python
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def reward_employee(employee, accomplishment):
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# 매 ❌ Tangible + expected (e.g., $100 for X)
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# 매 ✅ Verbal + specific
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feedback = f"""
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{employee.name}, your work on {accomplishment.project} was excellent.
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Specifically, your approach to {accomplishment.specific_thing} showed
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{accomplishment.competence_demonstrated}. This had {accomplishment.impact}.
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"""
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# 매 unexpected appreciation 의 OK
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if random.random() < 0.1:
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send_appreciation_card(employee, feedback)
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return feedback
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```
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### Recommendation system 의 autonomy preserve
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```python
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def recommend_with_autonomy(user, items):
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"""매 explainability + 매 user control."""
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recommendations = ml_model.recommend(user, items)
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return {
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'items': recommendations,
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'why': explain_each(recommendations, user), # 매 transparency
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'controls': {
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'less_of_this': lambda item: user.feedback_negative(item),
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'more_of_this': lambda item: user.feedback_positive(item),
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'turn_off_personalization': lambda: user.toggle_personalization(False),
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},
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}
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```
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### Mastery curve (competence)
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```python
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def mastery_journey(skill):
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return [
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('Novice', 'Hand-holding tutorial', 'high feedback'),
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('Advanced Beginner', 'Guided practice', 'frequent feedback'),
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('Competent', 'Independent task', 'less feedback'),
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('Proficient', 'Complex challenge', 'occasional feedback'),
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('Expert', 'Mastery + teaching', 'self-feedback'),
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]
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```
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### Anti-surveillance (autonomy)
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```python
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# 매 ❌ Productivity tracker showing keystrokes
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# 매 ✅ Self-tracker only the user sees
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class SelfProductivityTracker:
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"""매 user-only, opt-in."""
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def __init__(self, user_id):
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self.user_id = user_id
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self.private = True # 매 not shared with manager
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def log_focus_session(self, duration):
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self.sessions.append({'duration': duration, 'date': now()})
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def insights(self):
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# 매 user 의 only
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return generate_insights(self.sessions)
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```
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## 🤔 결정 기준
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| 상황 | Approach |
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|---|---|
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| Game | Autonomy (quest choice) + Competence (curve) + Relatedness (multiplayer) |
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| Education | Project choice + scaffolded + peer |
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| Workplace | Schedule + method + team |
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| Product onboard | Mastery feel + control |
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| Reward | Verbal + unexpected > tangible expected |
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| Productivity tool | Self-tracker > surveillance |
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**기본값**: 매 3 needs 의 audit + 매 reward design 의 careful.
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## 🔗 Graph
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- 부모: [[Psychology]] · [[Motivation]] · [[Behavioral-Economics]]
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- 변형: [[Self-Determination-Theory]] · [[Flow_State|Flow-State]]
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- 응용: [[Gamification]] · [[Game-Design]] · [[UX]]
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- Adjacent: [[Cognitive Biases]] · [[Addiction Neuroscience]] · [[Authenticity]] · [[Anthropomorphism]]
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## 🤖 LLM 활용
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**언제**: 매 motivation design. 매 game design. 매 education tool. 매 workplace policy. 매 product UX.
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**언제 X**: 매 manipulation / dark pattern.
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## ❌ 안티패턴
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- **Tangible expected reward 의 default**: 매 intrinsic 의 kill.
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- **Cash for fun**: 매 over-justification.
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- **Surveillance + autonomy 의 claim**: 매 violation.
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- **Difficulty curve X**: 매 competence ↓.
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- **No social feature** (suitable game): 매 relatedness X.
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- **Dark pattern (FOMO, sunk cost)**: 매 ethics violation.
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## 🧪 검증 / 중복
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- Verified (Deci-Ryan SDT, Pink "Drive", Lepper over-justification).
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- 신뢰도 A.
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- Related: [[Cognitive Biases]] · [[Addiction Neuroscience]] · [[Game-Design]] · [[Authenticity]] · [[Bounded_Rationality|Bounded-Rationality]].
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — 3 needs + over-justification + 매 quest / autonomy / mastery code |
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