---
id: wiki-2026-0508-dopamine
title: Dopamine
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Reward System, Reinforcement Signal, Prediction Error]
duplicate_of: none
source_trust_level: A
confidence_score: 0.85
verification_status: applied
tags: [neuroscience, reinforcement-learning, motivation, ux]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: python
  framework: rl
---

# Dopamine

## 매 한 줄
> **"매 reward prediction error 의 signal"**. 매 dopamine 의 modern view 는 pleasure 의 X, 매 *expected vs actual reward 의 차이* 의 broadcast. 매 Schultz (1997) 의 monkey VTA recording 의 RL 의 TD-error 의 isomorphism 의 establish. 매 product UX, addiction design, RL algorithm 의 shared substrate.

## 매 핵심

### 매 RPE (Reward Prediction Error)
- **Positive RPE**: 매 expected 보다 better. 매 dopamine burst.
- **Zero RPE**: 매 fully predicted. 매 baseline firing.
- **Negative RPE**: 매 expected 보다 worse. 매 firing dip.

### 매 RL 의 TD-error 와 의 mapping
- 매 δ = r + γV(s') − V(s).
- 매 dopamine neuron 의 firing rate 의 δ 의 encode (Schultz, Dayan, Montague 1997).

### 매 응용
1. Variable-ratio schedule (slot machine, social media feed) — 매 maximal RPE.
2. Habit formation (intermittent reward).
3. Anhedonia / addiction 의 dopaminergic dysregulation.
4. RL agent design (curiosity, intrinsic motivation).

## 💻 패턴

### TD-learning (dopamine-analog)
```python
import numpy as np

def td_update(V, s, r, s_next, alpha=0.1, gamma=0.9):
    """V: value table. δ = TD error = 'dopamine signal'."""
    delta = r + gamma * V[s_next] - V[s]   # ← RPE
    V[s] += alpha * delta
    return delta  # log this; it's the 'dopamine'

V = np.zeros(10)
for episode in range(1000):
    s, r, s_next = sample_transition()
    rpe = td_update(V, s, r, s_next)
```

### Curiosity-driven exploration (intrinsic dopamine analog)
```python
# Random Network Distillation (Burda 2018)
class RND(nn.Module):
    def __init__(self):
        super().__init__()
        self.target = nn.Sequential(nn.Linear(64, 128), nn.ReLU(), nn.Linear(128, 64))
        self.predictor = nn.Sequential(nn.Linear(64, 128), nn.ReLU(), nn.Linear(128, 64))
        for p in self.target.parameters(): p.requires_grad = False

    def intrinsic_reward(self, obs):
        with torch.no_grad():
            target = self.target(obs)
        pred = self.predictor(obs)
        return ((target - pred) ** 2).mean(-1)  # novelty bonus
```

### Variable-ratio schedule simulator
```python
def variable_ratio_session(p_reward=0.1, n_pulls=100):
    rpe_log = []
    expected = p_reward  # learned expectation
    for _ in range(n_pulls):
        r = 1.0 if np.random.rand() < p_reward else 0.0
        rpe = r - expected
        expected += 0.05 * rpe   # slow learning
        rpe_log.append(rpe)
    return rpe_log
# Pattern: high-amplitude RPE persists → "addictive" engagement
```

### Hyperbolic discounting (dopamine-future)
```python
def hyperbolic_value(reward, delay, k=0.1):
    """Real human/animal — closer to hyperbolic than exponential."""
    return reward / (1 + k * delay)
```

### Opponent process (reward + aversion)
```python
# Two-system: dopamine (reward) + serotonin (aversion / patience)
def dual_system_update(V_reward, V_aversion, r_pos, r_neg, s, s_next, alpha=0.1, gamma=0.9):
    delta_reward = r_pos + gamma * V_reward[s_next] - V_reward[s]
    delta_aversion = r_neg + gamma * V_aversion[s_next] - V_aversion[s]
    V_reward[s] += alpha * delta_reward
    V_aversion[s] += alpha * delta_aversion
    return delta_reward, delta_aversion
```

## 매 결정 기준

| 상황 | Insight |
|---|---|
| Habit-forming product | Variable-ratio reward (Slot machine schedule) |
| Sustained engagement | Mix predictable + unpredictable wins |
| Avoid burnout | Avoid pure RPE-maximization (ethical concern) |
| RL exploration stuck | Add intrinsic reward (RND, ICM) |
| Anhedonia in user | Reduce expectation, surprise with low-cost wins |

**기본값**: 매 RPE-aware design — but 매 ethics 의 weight (manipulation 의 risk).

## 🔗 Graph
- 부모: [[Reinforcement Learning]]
- 응용: [[Habit Formation]] · [[Game Design]] · [[Recommender Systems]]
- Adjacent: [[TD-Learning]] · [[Behavioral Economics]]

## 🤖 LLM 활용
**언제**: 매 product UX 의 retention mechanic 의 audit. 매 dark-pattern 의 detection.
**언제 X**: 매 clinical advice. 매 LLM 의 medical claim 의 X.

## ❌ 안티패턴
- **Dopamine = pleasure 의 simplification**: 매 X. 매 RPE 의 signal — pleasure 는 separate (opioid).
- **Pure exploitation (no novelty)**: 매 user 의 RPE 의 0 의 disengage.
- **Manipulative dark pattern**: 매 ethical violation. 매 design 의 audit 의 mandatory.

## 🧪 검증 / 중복
- Verified (Schultz 1997 Science, Sutton & Barto 2018, Berridge 2007).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — RPE / TD-learning isomorphism + UX implication |
