Cognitive Load Modeling™
Human Reading Pattern Simulation
Cognitive Load Modeling™ simulates how humans experience cognitive effort while reading. It models working memory load, attention decay, and reading fatigue — then restructures text to produce the natural ebb-and-flow of cognitive engagement that characterizes human-written content.
How It Works
Working Memory Load Estimation
Models the working memory burden of each sentence based on: clause depth, referential distance, syntactic unpredictability, and vocabulary rarity — using an attention-decay function.
Fatigue Pattern Detection
Detects segments with monotonically increasing cognitive load — a hallmark of AI text that "packs" information without regard for human reading capacity.
Load Redistribution
Splits high-load sentences, introduces explicit "relief" sentences after complex explanations, and adds bridging phrases that give readers cognitive anchors.
Engagement Variation
Injects attention-recovery patterns: short impactful sentences after dense paragraphs, rhetorical pauses, and concrete examples after abstractions — mirroring how skilled human writers maintain reader engagement.
Key Metrics
Use Cases
- Long-form content where reader fatigue degrades comprehension
- Educational material requiring varied cognitive engagement
- Marketing copy needing natural reader attention flow
- Technical documentation with accessible explanations
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