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title

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

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

💻 패턴

TD-learning (dopamine-analog)

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)

# 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

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)

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)

# 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

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