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Update Neo4j language tutor project with actual project image
- Add neo4j.jpg image to showcase the flagship language learning project - Update both homepage and projects page to use the new Neo4j image - Replace placeholder image references with actual project visualization - Maintain consistent branding across homepage and projects sections
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‎images/neo4j.jpg‎

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‎index.html‎

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@@ -51,13 +51,13 @@ <h1>Hi, I'm <span class="highlight">Will Hermann</span></h1>
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<h2>Featured Projects</h2>
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<div class="projects-grid">
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<div class="project-card">
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<img src="images/project1.jpg" alt="Project 1">
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<img src="images/neo4j.jpg" alt="Neo4j Language Learning Tutor">
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<div class="project-info">
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<h3>E-Commerce Platform</h3>
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<p>Full-stack web application built with React, Node.js, and MongoDB</p>
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<h3>Neo4j Language Learning Tutor</h3>
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<p>AI-powered conversational Russian tutor with real-time voice interaction and spaced repetition learning</p>
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<div class="project-links">
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<a href="#" class="btn btn-small">Live Demo</a>
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<a href="#" class="btn btn-small btn-outline">GitHub</a>
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<a href="https://github.com/coding-crying/realtime-agents-language-tutor" class="btn btn-small" target="_blank">GitHub</a>
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<a href="projects.html" class="btn btn-small btn-outline">View Details</a>
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</div>
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</div>
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</div>
@@ -80,7 +80,6 @@ <h3>Hoody - Intelligent Kitchen Assistant</h3>
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<p>Research project: AI-powered social robot for advanced culinary assistance</p>
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<div class="project-links">
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<a href="docs/social-robotics-report.pdf" class="btn btn-small" target="_blank">View Report</a>
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<a href="#" class="btn btn-small btn-outline">GitHub</a>
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</div>
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</div>
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</div>

‎projects.html‎

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@@ -35,12 +35,10 @@ <h1>My Projects</h1>
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<div class="container">
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<div class="filter-buttons">
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<button class="filter-btn active" data-filter="all">All Projects</button>
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<button class="filter-btn" data-filter="web">Web Apps</button>
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<button class="filter-btn" data-filter="mobile">Mobile Apps</button>
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<button class="filter-btn" data-filter="api">APIs</button>
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<button class="filter-btn" data-filter="tools">Tools</button>
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<button class="filter-btn" data-filter="web">AI Applications</button>
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<button class="filter-btn" data-filter="robotics">Robotics</button>
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<button class="filter-btn" data-filter="ml">Machine Learning</button>
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<button class="filter-btn" data-filter="tools">Research & Analysis</button>
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</div>
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</div>
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</section>
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<div class="project-card featured" data-category="web">
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<div class="project-image">
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<img src="images/language-tutor-app.jpg" alt="Neo4j Language Learning Tutor">
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<img src="images/neo4j.jpg" alt="Neo4j Language Learning Tutor">
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<div class="project-overlay">
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<div class="project-links">
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<a href="https://github.com/coding-crying/realtime-agents-language-tutor" class="btn btn-primary" target="_blank">GitHub</a>
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</div>
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</div>
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<div class="project-card" data-category="ml">
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<div class="project-image">
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<img src="images/ml-model-comparison.png" alt="Movie Score Prediction ML Model">
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<div class="project-overlay">
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<div class="project-links">
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<a href="https://github.com/MirkoRUG/Applied-ML-Group17" class="btn btn-primary" target="_blank">GitHub</a>
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</div>
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</div>
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</div>
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<div class="project-info">
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<h3>Movie Score Prediction ML System</h3>
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<p class="project-description">
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End-to-end machine learning pipeline using CatBoost regression to predict IMDb
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movie scores. Ensemble models learning achieved excellent performance with
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RMSE ~0.3, demonstrating comprehensive hyperparameter optimization and production deployment.
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</p>
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<div class="tech-stack">
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<span class="tech-tag">CatBoost</span>
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<span class="tech-tag">Optuna</span>
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<span class="tech-tag">FastAPI</span>
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<span class="tech-tag">Streamlit</span>
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<span class="tech-tag">Docker</span>
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<span class="tech-tag">Python</span>
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</div>
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<div class="project-features">
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<h4>Key Achievements:</h4>
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<ul>
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<li>Excellent ensemble model performance (RMSE ~0.3)</li>
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<li>17% performance improvement over baseline methods</li>
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<li>Bayesian hyperparameter optimization with 50 trials</li>
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<li>Production-ready FastAPI deployment with Docker</li>
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<li>Comprehensive evaluation with 5-fold cross-validation</li>
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<li>Interactive Streamlit web interface for predictions</li>
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<li>Robust preprocessing pipeline with outlier detection</li>
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</ul>
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</div>
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</div>
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</div>
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<div class="project-card" data-category="robotics">
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<div class="project-image">
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<img src="images/hoody-robot.jpg" alt="Hoody - Smart Kitchen Hood Robot">
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</div>
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</div>
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<div class="project-card" data-category="ml">
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<div class="project-image">
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<img src="images/ml-model-comparison.png" alt="Movie Score Prediction ML Model">
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<div class="project-overlay">
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<div class="project-links">
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<a href="https://github.com/MirkoRUG/Applied-ML-Group17" class="btn btn-primary" target="_blank">GitHub</a>
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</div>
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</div>
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</div>
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<div class="project-info">
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<h3>Movie Score Prediction ML System</h3>
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<p class="project-description">
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End-to-end machine learning pipeline using CatBoost regression to predict IMDb
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movie scores. Ensemble models learning achieved excellent performance with
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RMSE ~0.3, demonstrating comprehensive hyperparameter optimization and production deployment.
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</p>
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<div class="tech-stack">
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<span class="tech-tag">CatBoost</span>
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<span class="tech-tag">Optuna</span>
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<span class="tech-tag">FastAPI</span>
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<span class="tech-tag">Streamlit</span>
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<span class="tech-tag">Docker</span>
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<span class="tech-tag">Python</span>
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</div>
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<div class="project-features">
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<h4>Key Achievements:</h4>
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<ul>
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<li>Excellent ensemble model performance (RMSE ~0.3)</li>
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<li>17% performance improvement over baseline methods</li>
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<li>Bayesian hyperparameter optimization with 50 trials</li>
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<li>Production-ready FastAPI deployment with Docker</li>
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<li>Comprehensive evaluation with 5-fold cross-validation</li>
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<li>Interactive Streamlit web interface for predictions</li>
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<li>Robust preprocessing pipeline with outlier detection</li>
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</ul>
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</div>
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</div>
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</div>
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<div class="project-card" data-category="tools">
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<div class="project-image">
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<img src="images/patent-design.jpg" alt="Patented Braking System Design">
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</div>
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<div class="project-card" data-category="ml">
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<div class="project-info">
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<h3>Cognitive Modeling of Mind-Wandering and Rumination</h3>
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<p class="project-description">
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Research investigating spreading activation in emotional memory and its effects on
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mind-wandering episodes using ACT-R cognitive architecture. Explores how memory
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activation capacity influences rumination persistence and attention task performance.
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</p>
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<div class="tech-stack">
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<span class="tech-tag">ACT-R</span>
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<span class="tech-tag">Cognitive Modeling</span>
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<span class="tech-tag">SART</span>
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<span class="tech-tag">Memory Activation</span>
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<span class="tech-tag">Statistical Analysis</span>
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</div>
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<div class="project-features">
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<h4>Research Findings:</h4>
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<ul>
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<li>Computational model of rumination using spreading activation</li>
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<li>ANOVA analysis showing significant differences across conditions</li>
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<li>Evidence that higher activation capacity leads to persistent rumination</li>
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<li>Sustained Attention to Response Task (SART) simulation</li>
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<li>Quantitative support for negative memory bias theories</li>
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</ul>
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</div>
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<div class="project-links">
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<a href="Mind Wandering Cognitive Modelling.pdf" class="btn btn-primary" target="_blank">Download Paper</a>
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</div>
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</div>
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</div>
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<div class="project-card" data-category="tools">
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<div class="project-image">
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<img src="images/domain-analysis.jpg" alt="TrueSelect AI Recruitment Analysis">
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</div>
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</div>
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<div class="project-card" data-category="ml">
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<div class="project-info">
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<h3>Cognitive Modeling of Mind-Wandering and Rumination</h3>
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<p class="project-description">
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Research investigating spreading activation in emotional memory and its effects on
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mind-wandering episodes using ACT-R cognitive architecture. Explores how memory
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activation capacity influences rumination persistence and attention task performance.
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</p>
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<div class="tech-stack">
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<span class="tech-tag">ACT-R</span>
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<span class="tech-tag">Cognitive Modeling</span>
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<span class="tech-tag">SART</span>
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<span class="tech-tag">Memory Activation</span>
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<span class="tech-tag">Statistical Analysis</span>
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</div>
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<div class="project-features">
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<h4>Research Findings:</h4>
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<ul>
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<li>Computational model of rumination using spreading activation</li>
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<li>ANOVA analysis showing significant differences across conditions</li>
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<li>Evidence that higher activation capacity leads to persistent rumination</li>
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<li>Sustained Attention to Response Task (SART) simulation</li>
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<li>Quantitative support for negative memory bias theories</li>
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</ul>
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</div>
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<div class="project-links">
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<a href="Mind Wandering Cognitive Modelling.pdf" class="btn btn-primary" target="_blank">Download Paper</a>
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</div>
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</div>
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</div>
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</div>
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</div>
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</section>

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