For additional background, instructors (and students) can review the following non-exhaustive list of resources relevant to each of the modules included in this course.
| Run | View | |
|---|---|---|
| Tutorial: Jupyter Notebooks |  |
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| Tutorial: Python Basics | |
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| Tutorial: Working with Data | |
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| Exercise | |
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- Eshin Jolly - Intro to Jupyter Notebooks
- Python for Beginners
- Jake Vanderplas - Data Science Handbook
- Jake VanderPlas - Whirlwind Tour of Python
- Tal Yarkoni - Intro to Data Science w/ Python
- Yaroslav Halchenko - Intro to Programming for Psych/Neuro
- Collection of Jupyter Notebooks
- Tal Yarkoni - The homogenization of scientific computing, or why Python is steadily eating other languages’ lunch
- Shawn Rhoads - A brief introduction to Python for psychological science research
- Dominique Makowski - R or Python for Psychologists
- Russell Poldrack, et al. (2019) - Computational and informatics advances for reproducible data analysis in neuroimaging
- DartBrains - Intro to Programming
- DartBrains - Intro to Pandas
- DartBrains - Intro to Plotting
- Neuromatch Academy - Intro to Python Part I
- Neuromatch Academy - Intro to Python Part II
- NumPy Tutorials
- Pandas - Getting Started Tutorials
| Run | View | |
|---|---|---|
| Tutorial: Linear Modeling | |
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| Tutorial: Nonlinear Modeling | |
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| Exercise | |
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- Russell Poldrack - Statistical Thinking for the 21st Century
- Olivia Guest & Andrea Martin (2021) - How computational modeling can force theory building in psychological science
- Eshin Jolly & Luke Chang (2017) - The flatland fallacy: Moving beyond low dimensional thinking
- DartBrains - Introduction to the General Linear Model
- Lorraine Li - Introduction to Linear Regression in Python
- Roman Shemet - Manually computing the coefficients for an OLS regression using Python
- Neuromatch Academy - Vectors
- Neuromatch Academy - Matrices
- Neuromatch Academy - Linear Regression with MSE
- Neuromatch Academy - Linear Regression with MLE
- Neuromatch Academy - Multiple Linear Regression
| Run | View | |
|---|---|---|
| Tutorial: Two-Armed Bandits | |
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| Tutorial: Models of Learning | |
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| Exercise | |
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- Richard Sutton & Andrew Barto - Reinforcement Learning: An Introduction (2nd Edition)
- Robert Wilson & Anne Collins (2019) - Ten simple rules for the computational modeling of behavioral data
- Stefano Palminteri et al. (2017) - The importance of falsification in computational cognitive modeling
- Robert Wilson - NSCS 344 Modeling the Mind
- Jill O'Reilly & Hanneke den Ouden - Models of Learning (MATLAB)
- Shawn Rhoads - NSCI 526 Introduction to Reinforcement Learning
- @cloudssty - Gambling Game tutorial
- Neuromatch Academy - Learning to Predict
- Neuromatch Academy - Multi-Armed Bandits
- Computational Models of Brain and Behavior
| Run | View | |
|---|---|---|
| Tutorial: Prosocial Learning | |
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| Exercise | |
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- Patricia Lockwood, et al. (2020) - Is there a “social” brain? Implementations and algorithms
- Lei Zhang, Lukas Lengersdorff, et al. (2020) - Using reinforcement learning models in social neuroscience: Frameworks, pitfalls and suggestions of best practices (MATLAB & R code)
- Patricia Lockwood & Miriam Klein-Flügge (2020) - Computational modelling of social cognition and behaviour—a reinforcement learning primer
- Shinsuke Suzuki & John O’Doherty (2020) - Breaking human social decision making into multiple components and then putting them together again
- Jin Hyun Cheong et al. (2017) - Computational Models in Social Neuroscience