Lab Resources
We believe in open science and knowledge sharing. Here you'll find software, datasets, educational materials, and other resources developed by our lab and collaborators.
Software & Code
Our lab develops open-source software for computational cognitive science research. All code is available on GitHub with documentation and examples.
CogModel Toolkit
Python library for cognitive modeling
A comprehensive Python toolkit for building and analyzing computational models of cognition. Includes implementations of popular models for memory, attention, and decision making.
Features:
- Bayesian inference tools
- Neural network models
- Parameter estimation utilities
- Visualization functions
WorkingMemory Simulator
Neural network model of working memory
Implementation of our biologically-realistic working memory model that incorporates neural oscillations and synaptic plasticity. Includes parameter fitting and analysis tools.
Applications:
- Capacity limitation studies
- Individual differences analysis
- Interference effect modeling
- Neural data fitting
AttentionNet
Attention models for visual processing
Deep learning models of visual attention based on cognitive theories. Includes implementation of attention mechanisms for scene understanding and object recognition tasks.
Models included:
- Spatial attention networks
- Feature-based attention
- Object-based attention
- Attention-memory integration
CausalLearning Framework
Bayesian causal inference tools
Tools for modeling human causal learning using Bayesian networks and structure learning algorithms. Supports both experimental data analysis and model simulation.
Features:
- Bayesian network inference
- Structure learning algorithms
- Intervention analysis
- Developmental modeling
Datasets
We make our experimental data available to support reproducible research and enable new discoveries. All datasets include detailed documentation and analysis code.
Working Memory Capacity Database
N = 500 participants | Behavioral + EEG
Large-scale dataset combining behavioral measures of working memory capacity with EEG recordings during various memory tasks. Includes individual difference measures and demographic information.
Tasks included:
- Change detection paradigm
- n-back task variations
- Complex span tasks
- Interference resolution
Visual Attention in Scenes
N = 200 participants | Eye tracking + fMRI
Eye tracking and neuroimaging data from experiments on visual attention during natural scene viewing. Includes scene images, fixation data, and neural responses.
Data types:
- High-resolution eye tracking
- fMRI BOLD responses
- Scene annotations
- Behavioral responses
Decision Making Under Uncertainty
N = 300 participants | Online experiments
Behavioral data from multi-armed bandit tasks and risky choice experiments. Includes trial-by-trial data, reaction times, and computational model fits.
Experiments:
- Restless bandit tasks
- Probability learning
- Risk preference tasks
- Temporal discounting
Metacognition Battery
N = 400 participants | Cross-sectional
Comprehensive battery of metacognitive tasks across multiple domains including memory, perception, and problem solving. Includes confidence judgments and strategy reports.
Domains assessed:
- Metamemory
- Metacognitive monitoring
- Strategy selection
- Confidence calibration
Educational Materials
Resources for learning about computational cognitive science, including tutorials, course materials, and interactive demonstrations.
Interactive Demos
Web-based interactive demonstrations of cognitive phenomena and computational models.
- Working memory capacity limits
- Attention and visual search
- Bayesian inference
- Neural network learning
Course Materials
Lecture slides, assignments, and readings from our graduate courses in computational cognitive science.
- Computational Modeling
- Bayesian Cognition
- Neural Networks & Cognition
- Research Methods
Video Tutorials
Step-by-step video tutorials on computational methods and analysis techniques.
- Model fitting in Python
- Bayesian data analysis
- EEG/fMRI analysis
- Open science practices
Lab Protocols & Methods
Detailed protocols and methodological resources for conducting computational cognitive science research.
Experimental Protocols
Standardized procedures for conducting cognitive experiments and data collection.
- Behavioral testing protocols
- EEG recording procedures
- Eye tracking setup guides
- Online experiment design
Analysis Pipelines
Computational workflows and analysis pipelines for processing cognitive science data.
- Preprocessing pipelines
- Statistical analysis templates
- Model comparison frameworks
- Visualization tools
Computational Tools
Software tools and computational resources for cognitive modeling and analysis.
- Model fitting toolboxes
- Simulation frameworks
- Validation procedures
- Reproducibility guidelines
Best Practices
Guidelines and best practices for conducting rigorous computational cognitive science.
- Preregistration templates
- Open science workflows
- Reproducible research
- Ethics guidelines
Contributing & Collaboration
We welcome contributions to our open science initiatives and are always interested in new collaborations.
How to Contribute
Interested in contributing to our open source projects? We welcome bug reports, feature requests, and code contributions from the research community.
- Submit issues on GitHub
- Contribute code improvements
- Share your datasets
- Improve documentation
Request Resources
Looking for specific resources or have questions about our materials? We're happy to help with implementation, provide additional documentation, or discuss potential collaborations.
- Technical support
- Custom analysis requests
- Collaboration opportunities
- Training workshops