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Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

2026-05-20 23:52:15 +09:00

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title

Creativity Research

매 한 줄

"매 creativity 의 measurable cognitive process — 매 mystical talent 아님". 매 1950 Guilford APA address 가 field 의 launch — 매 divergent thinking, fluency, originality 의 quantifiable. 매 2026 의 LLM-augmented co-creation, fMRI 의 default mode network 연구, computational creativity 의 active.

매 핵심

매 4P framework (Rhodes 1961)

Person: 매 traits — openness, tolerance for ambiguity, intrinsic motivation.
Process: 매 stages — preparation → incubation → illumination → verification (Wallas 1926).
Product: 매 novel + useful (Stein 1953 의 standard definition).
Press: 매 environment — domain, field gatekeepers (Csikszentmihalyi systems model).

매 측정 (psychometrics)

TTCT (Torrance Tests of Creative Thinking): 매 fluency, flexibility, originality, elaboration.
AUT (Alternative Uses Task): 매 brick 의 uses 나열 — 매 divergent thinking 의 standard.
CAT (Consensual Assessment Technique, Amabile): 매 expert judges 의 product rating.
RAT (Remote Associates): 매 convergent creativity (3 cue → 1 link word).

매 응용

K-12 design thinking curriculum.
매 R&D ideation workshop (IDEO 의 protocols).
매 LLM prompt engineering 의 creativity scaffolding.

💻 패턴

Divergent thinking score (AUT)

def aut_score(responses: list[str], reference_corpus: dict[str, int]) -> dict:
    """Score divergent-thinking output: fluency, flexibility, originality."""
    fluency = len(responses)
    categories = {classify_category(r) for r in responses}
    flexibility = len(categories)
    # originality = 1 - frequency in reference corpus (lower freq = more original)
    total = sum(reference_corpus.values()) or 1
    originality = sum(
        1 - (reference_corpus.get(r.lower(), 0) / total) for r in responses
    ) / max(fluency, 1)
    return {"fluency": fluency, "flexibility": flexibility, "originality": originality}

LLM-augmented divergent ideation

from anthropic import Anthropic

client = Anthropic()

def co_creative_ideation(prompt: str, n: int = 20) -> list[str]:
    """Use Claude as a divergent-thinking partner — temperature high for variance."""
    msg = client.messages.create(
        model="claude-opus-4-7",
        max_tokens=2000,
        temperature=1.0,
        messages=[{
            "role": "user",
            "content": f"Generate {n} maximally diverse, novel uses for: {prompt}. "
                       f"Span categories. Avoid clichés. One per line."
        }],
    )
    return [line.strip("- ") for line in msg.content[0].text.splitlines() if line.strip()]

Consensual Assessment (CAT) aggregation

import numpy as np
from scipy.stats import pearsonr

def cat_reliability(ratings: np.ndarray) -> float:
    """Inter-rater reliability via Cronbach's alpha across expert judges."""
    k = ratings.shape[1]
    item_var = ratings.var(axis=0, ddof=1).sum()
    total_var = ratings.sum(axis=1).var(ddof=1)
    return (k / (k - 1)) * (1 - item_var / total_var)

Incubation effect simulation

def incubation_benefit(initial_attempt_score: float, incubation_minutes: int) -> float:
    """Sio & Ormerod 2009 meta-analysis: ~0.3 SD boost after incubation."""
    if incubation_minutes < 5:
        return initial_attempt_score
    return initial_attempt_score + 0.3 * min(incubation_minutes / 30, 1.0)

Default Mode Network proxy (resting-state correlation)

def dmn_creativity_correlation(dmn_connectivity: float, ecn_connectivity: float) -> float:
    """Beaty et al. 2018: high creativity = strong DMN ↔ ECN coupling."""
    return dmn_connectivity * ecn_connectivity  # simplified product proxy

Equivalence-class feature (Mednick RAT)

def remote_associates_solve(cues: tuple[str, str, str], assoc_db: dict) -> str | None:
    """Find a single word that associates with all three cues."""
    sets = [set(assoc_db.get(c, [])) for c in cues]
    common = set.intersection(*sets)
    return next(iter(common), None)

매 결정 기준

상황	Approach
Quick classroom screen	TTCT short form
Real-world product creativity	CAT with 3+ domain experts
Lab divergent thinking	AUT + originality corpus
Insight problem solving	RAT or compound remote associates
LLM augmentation	high-temperature ideation + human convergent filter

기본값: 매 AUT + CAT for research; 매 LLM-as-divergent-partner + human-as-convergent-filter for applied work.

🔗 Graph

부모: Cognitive Psychology
변형: Divergent Thinking · Convergent Thinking · Computational_Creativity
응용: Design Thinking · Brainstorming
Adjacent: Default Mode Network

🤖 LLM 활용

언제: 매 divergent ideation phase — 매 broad space exploration, 매 cliché breaking, 매 cross-domain analogies. 언제 X: 매 convergent evaluation alone — 매 LLM 의 novelty calibration 의 약함 (training data bias toward common). 매 originality scoring 시 의 corpus-based metric 결합 필요.

❌ 안티패턴

Brainstorming = creativity 의 동일시: 매 group brainstorming 의 production blocking — 매 nominal groups 가 실제로 더 많은 ideas (Diehl & Stroebe 1987).
Originality 만 추적: 매 useful 의 손실 — 매 novel + useful 가 정의.
Single judge CAT: 매 inter-rater reliability 의 unverifiable.
TTCT 만 의 의존: 매 ecological validity 의 약함 — real-world creative achievement prediction 의 modest (r ≈ 0.2-0.3).

🧪 검증 / 중복

Verified (Guilford 1950, Torrance 1966, Amabile 1982, Beaty et al. 2018 NeuroImage).
신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — 4P framework, AUT/CAT/RAT measurement, LLM co-creation patterns 추가

6.3 KiB Raw Blame History