---
id: wiki-2026-0508-cognitive-evaluation-theory
title: Cognitive Evaluation Theory (Self-Determination)
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [self-determination theory, SDT, autonomy, competence, relatedness, intrinsic motivation, over-justification]
duplicate_of: none
source_trust_level: A
confidence_score: 0.88
verification_status: applied
tags: [psychology, motivation, sdt, intrinsic-motivation, gamification, game-design, education, productivity]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: psychology
  applicable_to: [Game Design, Education, Workplace, Product UX]
---

# Cognitive Evaluation Theory / SDT

## 매 한 줄
> **"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.

## 매 핵심

### Self-Determination Theory 의 3 needs
1. **Autonomy**: 매 self-chosen.
2. **Competence**: 매 mastery feel.
3. **Relatedness**: 매 connection.

→ 매 3 의 satisfy = 매 intrinsic motivation.

### Intrinsic vs Extrinsic
- **Intrinsic**: 매 enjoyment / interest 의 itself.
- **Extrinsic**: 매 reward / punishment.
- **Internalization**: 매 extrinsic 의 internalize 의 spectrum.

### Over-justification effect (Lepper)
- 매 paid for activity 의 enjoy → 매 intrinsic 의 lose.
- 매 famous: 매 kid drawing experiment.
- 매 implication: 매 reward 의 careful design.

### 매 reward 의 effect
| Type | Effect |
|---|---|
| Tangible + expected | 매 intrinsic ↓ |
| Verbal positive | 매 intrinsic ↑ |
| Unexpected | 매 less harmful |
| Task-contingent | 매 intrinsic ↓ |
| Performance-contingent (informative) | 매 mid |
| Choice-supportive | 매 intrinsic ↑ |

### 매 application

#### Game design
- **Autonomy**: 매 quest choice, 매 customization.
- **Competence**: 매 progression, 매 skill curve.
- **Relatedness**: 매 multiplayer, 매 guild.

#### Education
- **Autonomy**: 매 project topic choice.
- **Competence**: 매 scaffolded difficulty.
- **Relatedness**: 매 peer collab.

#### Workplace
- **Autonomy**: 매 schedule, 매 method.
- **Competence**: 매 challenging task + feedback.
- **Relatedness**: 매 team belonging.

#### Product UX
- **Autonomy**: 매 customization, 매 control.
- **Competence**: 매 onboarding 의 mastery.
- **Relatedness**: 매 social feature.

### 매 dark side (anti-pattern)
- 매 tangible reward 의 dominate (cash, gift card).
- 매 surveillance / monitoring → autonomy ↓.
- 매 forced ranking → relatedness ↓.
- 매 over-difficult / under-difficult → competence ↓.

### 매 modern AI 의 의미
- **AI assistant**: 매 user 의 competence 의 reduce 의 risk.
- **Recommendation**: 매 autonomy 의 illusion vs reality.
- **Gamification**: 매 manipulation 의 risk (dark pattern).
- **Productivity tracker**: 매 surveillance.

## 💻 패턴 (응용)

### Game progression (autonomy + competence)
```ts
class QuestSystem {
  // 매 autonomy: 매 player 의 choose
  getAvailableQuests(player) {
    return this.quests.filter(q => 
      q.unlock(player) && 
      !q.completed(player)
    );  // 매 multiple option
  }
  
  // 매 competence: 매 skill curve
  recommendNextQuest(player) {
    const available = this.getAvailableQuests(player);
    const skillLevel = player.estimatedSkill();
    
    // 매 zone of proximal development
    return available
      .filter(q => q.difficulty >= skillLevel - 1 && q.difficulty <= skillLevel + 2)
      .sort((a, b) => a.difficulty - b.difficulty)[0];
  }
}
```

### Workplace autonomy (4-day workweek-style)
```python
def autonomy_audit(team):
    return {
        'schedule_flexibility': measure(team, 'self-set start/end times'),
        'method_flexibility': measure(team, 'choose how to solve'),
        'topic_flexibility': measure(team, 'pick what to work on'),
        'tool_flexibility': measure(team, 'choose tools'),
        'location_flexibility': measure(team, 'remote / hybrid'),
    }
```

### Educational scaffolding (competence)
```python
def adaptive_difficulty(student, current_skill, performance):
    """매 zone of proximal development."""
    if performance > 0.85:
        return current_skill + 1  # 매 challenge ↑
    elif performance < 0.5:
        return current_skill - 1  # 매 ease
    return current_skill  # 매 stable
```

### Avoid over-justification (verbal > tangible)
```python
def reward_employee(employee, accomplishment):
    # 매 ❌ Tangible + expected (e.g., $100 for X)
    # 매 ✅ Verbal + specific
    feedback = f"""
    {employee.name}, your work on {accomplishment.project} was excellent.
    Specifically, your approach to {accomplishment.specific_thing} showed
    {accomplishment.competence_demonstrated}. This had {accomplishment.impact}.
    """
    
    # 매 unexpected appreciation 의 OK
    if random.random() < 0.1:
        send_appreciation_card(employee, feedback)
    
    return feedback
```

### Recommendation system 의 autonomy preserve
```python
def recommend_with_autonomy(user, items):
    """매 explainability + 매 user control."""
    recommendations = ml_model.recommend(user, items)
    
    return {
        'items': recommendations,
        'why': explain_each(recommendations, user),  # 매 transparency
        'controls': {
            'less_of_this': lambda item: user.feedback_negative(item),
            'more_of_this': lambda item: user.feedback_positive(item),
            'turn_off_personalization': lambda: user.toggle_personalization(False),
        },
    }
```

### Mastery curve (competence)
```python
def mastery_journey(skill):
    return [
        ('Novice', 'Hand-holding tutorial', 'high feedback'),
        ('Advanced Beginner', 'Guided practice', 'frequent feedback'),
        ('Competent', 'Independent task', 'less feedback'),
        ('Proficient', 'Complex challenge', 'occasional feedback'),
        ('Expert', 'Mastery + teaching', 'self-feedback'),
    ]
```

### Anti-surveillance (autonomy)
```python
# 매 ❌ Productivity tracker showing keystrokes
# 매 ✅ Self-tracker only the user sees

class SelfProductivityTracker:
    """매 user-only, opt-in."""
    def __init__(self, user_id):
        self.user_id = user_id
        self.private = True  # 매 not shared with manager
    
    def log_focus_session(self, duration):
        self.sessions.append({'duration': duration, 'date': now()})
    
    def insights(self):
        # 매 user 의 only
        return generate_insights(self.sessions)
```

## 🤔 결정 기준
| 상황 | Approach |
|---|---|
| Game | Autonomy (quest choice) + Competence (curve) + Relatedness (multiplayer) |
| Education | Project choice + scaffolded + peer |
| Workplace | Schedule + method + team |
| Product onboard | Mastery feel + control |
| Reward | Verbal + unexpected > tangible expected |
| Productivity tool | Self-tracker > surveillance |

**기본값**: 매 3 needs 의 audit + 매 reward design 의 careful.

## 🔗 Graph
- 부모: [[Psychology]] · [[Motivation]] · [[Behavioral-Economics]]
- 변형: [[Self-Determination-Theory]] · [[Flow_State|Flow-State]]
- 응용: [[Gamification]] · [[Game-Design]] · [[UX]]
- Adjacent: [[Cognitive-Biases]] · [[Addiction-Neuroscience]] · [[Authenticity]] · [[Anthropomorphism]]

## 🤖 LLM 활용
**언제**: 매 motivation design. 매 game design. 매 education tool. 매 workplace policy. 매 product UX.
**언제 X**: 매 manipulation / dark pattern.

## ❌ 안티패턴
- **Tangible expected reward 의 default**: 매 intrinsic 의 kill.
- **Cash for fun**: 매 over-justification.
- **Surveillance + autonomy 의 claim**: 매 violation.
- **Difficulty curve X**: 매 competence ↓.
- **No social feature** (suitable game): 매 relatedness X.
- **Dark pattern (FOMO, sunk cost)**: 매 ethics violation.

## 🧪 검증 / 중복
- Verified (Deci-Ryan SDT, Pink "Drive", Lepper over-justification).
- 신뢰도 A.
- Related: [[Cognitive-Biases]] · [[Addiction-Neuroscience]] · [[Game-Design]] · [[Authenticity]] · [[Bounded_Rationality|Bounded-Rationality]].

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — 3 needs + over-justification + 매 quest / autonomy / mastery code |