2nd/10_Wiki/Topics/Domain_General/From_Other/Habit-Formation.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-habit-formation	Habit Formation	10_Wiki/Topics	verified	self	Habit Loop Behavior Automation Habituation	none	A	0.9	applied	psychology behavior neuroscience habits		2026-05-10	pending	language framework en behavioral-psychology

Habit Formation

매 한 줄

"매 habit = cue → routine → reward 의 basal-ganglia automatization". 매 21-day myth 의 false — Lally 2010 의 median 66 days (range 18-254). 매 2026 의 wearables + LLM coaching + JITAI (just-in-time adaptive intervention) 의 active.

매 핵심

매 habit loop (Duhigg / Wood)

1. Cue: 매 trigger — time, place, emotional state, preceding action, people.
2. Routine: 매 behavior 자체.
3. Reward: 매 reinforcement — neural prediction error.
4. Craving (Wood addition): 매 anticipation 의 cue→reward.

매 neural substrate

• Goal-directed: 매 prefrontal + dorsomedial striatum — early learning.
• Habitual: 매 dorsolateral striatum (sensorimotor loop) — automatization.
• Switch: 매 overtraining + stable context → habitual takeover.

매 formation 의 핵심 levers

• Implementation intentions (Gollwitzer): "When X, I will Y" — 매 효과 size large (d ≈ 0.65).
• Context stability: 매 same time + place 의 consistency.
• Friction reduction: 매 cue salience ↑, 매 obstacle ↓.
• Temptation bundling (Milkman): 매 desired + pleasurable 결합.
• Identity-based: 매 "I am someone who..." (Clear).

매 응용

1. Atomic Habits 의 4 laws (obvious, attractive, easy, satisfying).
2. Health behavior change (exercise, medication adherence).
3. Productivity (deep-work blocks).
4. Habit-stacking (after-X-then-Y).

💻 패턴

Habit tracker (streak + context)

from dataclasses import dataclass, field
from datetime import date, timedelta

@dataclass
class HabitLog:
    name: str
    cue_context: dict           # {time, location, preceding_action}
    completions: list[date] = field(default_factory=list)

    @property
    def streak(self) -> int:
        if not self.completions:
            return 0
        s, today = 1, max(self.completions)
        for i in range(1, len(self.completions)):
            if today - self.completions[-1 - i] == timedelta(days=i):
                s += 1
            else:
                break
        return s

Implementation intention generator

def implementation_intention(goal: str, cue: str, action: str) -> str:
    return f"When {cue}, I will {action} in service of {goal}."

# When I pour my morning coffee, I will do 10 push-ups in service of strength training.

Habit-stacking chain

def stack(anchor: str, new_habit: str, reward: str | None = None) -> dict:
    return {
        "anchor": anchor,
        "new_habit": new_habit,
        "rule": f"After {anchor}, I will {new_habit}.",
        "immediate_reward": reward,
    }

JITAI delivery decision

def jitai_should_deliver(state: dict) -> bool:
    """Deliver intervention only when receptive + context-matched + low burden."""
    return (state["stress"] < 0.7
            and state["cognitive_load"] < 0.6
            and state["context_match"] > 0.8
            and state["recent_interventions_24h"] < 3)

Lally formation curve

import numpy as np

def automaticity(day: int, asymptote: float = 0.95, k: float = 0.04) -> float:
    """Asymptotic automaticity (Lally 2010 fit)."""
    return asymptote * (1 - np.exp(-k * day))
# day 21 → ~0.58, day 66 → ~0.88

Context-cue salience score

def cue_salience(cue: dict, history: list[dict]) -> float:
    """Higher when cue co-occurs with successful routine."""
    matches = [h for h in history if all(h.get(k) == v for k, v in cue.items())]
    if not matches:
        return 0.0
    return sum(h["completed"] for h in matches) / len(matches)

매 결정 기준

상황	Strategy
Brand-new habit	implementation intention + context stability
Existing routine + new addition	habit stacking (anchor)
High-friction habit	reduce friction first, then add cue
Reward-poor habit	temptation bundling
Identity-level change	identity-based ("I am the kind of person who...")
Relapse prevention	re-stabilize context, restore cue

기본값: 매 implementation intention + same context daily + 60-90 day window. 매 21-day promise X.

🔗 Graph

• 부모: Operant_Conditioning
• 변형: Habit Stacking
• 응용: CBT
• Adjacent: Basal Ganglia · Self-Determination Theory

🤖 LLM 활용

언제: 매 implementation intention drafting, 매 habit-stack anchor 의 brainstorm, 매 obstacle anticipation, 매 daily reflection scaffold. 언제 X: 매 individual psychological diagnosis (e.g., compulsion vs habit) — 매 clinical professional 필수.

❌ 안티패턴

• 21-day promise: 매 individual variance 무시 — 매 18-254 day range.
• Willpower 만 의 의존: 매 ego depletion + decision fatigue — 매 environment design.
• Multiple new habits 의 동시: 매 cognitive bandwidth 초과 — 매 1-2 habits 의 sequence.
• No cue specification: 매 vague intention ("eat better") — 매 specific cue + action.
• Punishing missed days excessively: 매 self-shame spiral — 매 "miss once, never twice" rule.

🧪 검증 / 중복

• Verified (Lally et al. 2010 EJSP, Wood "Good Habits, Bad Habits" 2019, Duhigg "Power of Habit", Clear "Atomic Habits", Gollwitzer 1999 meta-analysis).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — habit loop, Lally curve, JITAI, implementation intentions 추가