[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CWPNGRASS6MD2D8HFNWH26":3,"skill-same:01M6CWPNGRASS6MD2D8HFNWH26":61},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":23,"linkOnly":24,"markdownContent":25,"markdownBody":26,"frontMatter":27,"refName":31,"commitSha":32,"skillDirectory":5,"forksCount":33,"repositoryHtmlUrl":34,"repositoryHomepageUrl":6,"ownerHtmlUrl":35,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":36,"install":37,"resources":50},"01M6CWPNGRASS6MD2D8HFNWH26","cognitive-science",null,"Cognitive science 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models\n- Improving AI interpretability\n\n## Key Concepts\n\n### Cognitive Principles for AI\n- **Mental Models**: Users have internal representations\n- **Attention**: Limited capacity, needs prioritization\n- **Memory**: Working memory constraints, chunking\n- **Decision Making**: Bounded rationality\n\n### Cognitive Load in UI\n```python\n# Principles to reduce cognitive load\ndesign_principles = {\n    \"progressive_disclosure\": \"Show complexity gradually\",\n    \"chunking\": \"Group related items (7±2)\",\n    \"visual_hierarchy\": \"Guide attention\",\n    \"consistency\": \"Reduce learning\",\n    \"feedback\": \"Confirm actions\",\n    \"recognition_vs_recall\": \"Minimize memory load\"\n}\n```\n\n### User Mental Models\n- Match system to user's conceptual model\n- Use familiar metaphors\n- Provide clear affordances\n- Enable undo and recovery\n- Manage expectations\n\n### Applying Cognitive Science\n- **Information Processing**: Input → Processing → Output\n- **Dual Process Theory**: System 1 (fast, intuitive) + System 2 (slow, deliberate)\n- **Gestalt Principles**: Proximity, similarity, continuity\n- **Fitts's Law**: Time to reach target depends on distance\u002Fsize\n","## What I do\n\n- Apply cognitive science to AI design\n- Understand human cognition for better UX\n- Design human-centered AI systems\n- Model cognitive processes\n- Create intuitive interfaces\n\n## When to use me\n\nUse me when:\n- Building human-centered AI\n- Designing intuitive interfaces\n- Understanding user mental models\n- Improving AI interpretability\n\n## Key Concepts\n\n### Cognitive Principles for AI\n- **Mental Models**: Users have internal representations\n- **Attention**: Limited capacity, needs prioritization\n- **Memory**: Working memory constraints, chunking\n- **Decision Making**: Bounded rationality\n\n### Cognitive Load in UI\n```python\n# Principles to reduce cognitive load\ndesign_principles = {\n    \"progressive_disclosure\": \"Show complexity gradually\",\n    \"chunking\": \"Group related items (7±2)\",\n    \"visual_hierarchy\": \"Guide attention\",\n    \"consistency\": \"Reduce learning\",\n    \"feedback\": \"Confirm actions\",\n    \"recognition_vs_recall\": \"Minimize memory load\"\n}\n```\n\n### User Mental Models\n- Match system to user's conceptual model\n- Use familiar metaphors\n- Provide clear affordances\n- Enable undo and recovery\n- Manage expectations\n\n### Applying Cognitive Science\n- **Information Processing**: Input → Processing → Output\n- **Dual Process Theory**: System 1 (fast, intuitive) + System 2 (slow, deliberate)\n- **Gestalt Principles**: Proximity, similarity, continuity\n- **Fitts's Law**: Time to reach target depends on 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