---
id: wiki-2026-0508-weak-central-coherence-theory
title: Weak Central Coherence Theory
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [WCC, Detail-Focused Cognition, Frith-Happé Theory]
duplicate_of: none
source_trust_level: A
confidence_score: 0.85
verification_status: applied
tags: [cognitive-science, autism, perception, psychology]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: Theory
  framework: Cognitive Psychology
---

# Weak Central Coherence Theory

## 매 한 줄
> **"매 detail 의 see, gestalt 의 miss"**. Uta Frith (1989) "Autism: Explaining the Enigma" — 매 autism cognition 의 perceptual style theory; 매 strong local processing + weak global integration. 2026 의 reframed as "cognitive style" rather than deficit (Happé & Frith 2006 update).

## 매 핵심

### 매 핵심 claim
- Neurotypical cognition: bias toward global / gestalt / contextual integration.
- Autistic cognition (per WCC): bias toward local / detail / piecemeal processing.
- WCC ≠ deficit only — 매 trade-off: detail strength + global integration weakness.

### 매 Evidence (classic)
- Block Design test: autistic individuals often faster — 매 disembedding ability.
- Embedded Figures Test: superior performance.
- Müller-Lyer illusion: less susceptible (less global context bias).
- Ebbinghaus illusion: similar — context affects local size judgment less.
- Reading: superior at proofreading (catching local typos), weaker at gist.

### 매 Modern updates (Happé & Frith 2006)
- WCC = bias, not inability — global processing achievable with prompts.
- Two-factor model: local enhancement + global processing weakness 의 partially independent.
- Compatible with Theory of Mind / Executive Function deficit theories (multi-deficit view).
- Neuro-imaging: reduced top-down feedback in autism (Mottron's Enhanced Perceptual Functioning).

### 매 응용
1. Autism cognitive profiling.
2. Education design (sensory-friendly, structured).
3. UX for neurodivergent users.
4. Strengths-based employment (QA, debugging, pattern detection).
5. AI cognitive modeling (fine-grained vision tasks ≈ WCC profile).

## 💻 패턴

### Embedded Figures Test (digital)
```python
import matplotlib.pyplot as plt
import numpy as np

def generate_eft_trial(target_shape, distractor_density=0.7):
    """Hide simple shape inside complex figure."""
    canvas = np.random.rand(100, 100) < distractor_density
    # Embed target shape (e.g., triangle) at random location
    x, y = np.random.randint(20, 70, 2)
    canvas[y:y+10, x:x+10] = embed(target_shape)
    return canvas, (x, y)

# Measure: time to find target
# WCC profile: faster, especially with high distractor density
```

### Navon hierarchical letter task
```python
# Big "H" made of small "S"s
def navon_stimulus(global_letter, local_letter, size=10):
    grid = render_letter(global_letter, size)
    return [[local_letter if cell else ' ' for cell in row] for row in grid]

# Local task: report small letters
# Global task: report big letter
# Local-precedence (faster local) suggests WCC profile
```

### Block Design simulation
```python
def block_design_score(target_pattern, time_limit=60):
    """
    Subject reproduces 2D pattern with 3D blocks.
    WCC profile: faster, especially when pattern segmentation helps.
    """
    start = time.time()
    while not match(reconstructed, target_pattern):
        if time.time() - start > time_limit:
            return None
    return time.time() - start
```

### Strengths-based assessment
```python
profile = {
    "local_processing": 0.92,     # high
    "global_integration": 0.45,   # lower
    "detail_memory": 0.88,
    "pattern_recognition": 0.90,
    "gestalt_perception": 0.40,
}

# Map to occupational fit
strengths = ["QA testing", "Code review", "Data validation", "Pattern detection"]
challenges = ["Executive summaries", "Big-picture strategy"]
```

### LLM analogy (2026)
```python
# Vision encoder (CLIP, SAM) ≈ strong local feature extraction
# LLM gist summarization ≈ global integration
# Some VLMs (2026) explicitly bias toward local detail tasks
# Autistic strength profile ≈ "local-detail-tuned vision encoder + light gist head"
```

## 매 결정 기준
| Context | Approach |
|---|---|
| Diagnosing autism cognitive style | Battery: EFT + Block Design + Navon + reading-gist |
| Designing UX | Provide structure, reduce gestalt-only cues |
| Educational support | Explicit gist + leverage detail strength |
| Employment matching | Detail-heavy roles (QA, accounting, ML data work) |

**기본값**: Treat as cognitive style (strengths + trade-offs), use multi-test battery.

## 🔗 Graph
- Adjacent: [[Theory of Mind]] · [[Working Memory]]

## 🤖 LLM 활용
**언제**: Modeling cognitive style differences. Designing assessment / UX for neurodivergent. Explaining detail-vs-gist trade-off in cognition.
**언제 X**: Diagnostic claim of autism — WCC alone is insufficient; multi-theory + clinical assessment required.

## ❌ 안티패턴
- **WCC = autism**: 매 single-theory account 의 X — 매 ToM / EF / Monotropism 의 complement.
- **Deficit framing**: 2026 view 의 cognitive style + trade-off — pure deficit framing 의 outdated.
- **Universal application**: 매 autism heterogeneity — WCC profile 의 X every autistic individual 의 fit.
- **Conflating WCC with low IQ**: WCC 의 IQ-orthogonal — high-IQ autistic individuals show classic WCC profile.

## 🧪 검증 / 중복
- Verified (Frith 1989; Happé & Frith 2006 update; Mottron EPF 2006; ongoing replication 2010s-2020s).
- 신뢰도 A- (theory contested but well-supported empirical phenomena).

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — WCC with EFT/Navon/Block Design tasks, 2026 cognitive-style reframing |
