Teaching Resources

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.

Module 01 - Scientific Python

	Run	View
Tutorial: Jupyter Notebooks
Tutorial: Python Basics
Tutorial: Working with Data
Exercise

• 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

Module 02 - Introduction to Modeling

	Run	View
Tutorial: Linear Modeling
Tutorial: Nonlinear Modeling
Exercise

• 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

Module 03 - Reinforcement Learning

	Run	View
Tutorial: Two-Armed Bandits
Tutorial: Models of Learning
Exercise

• 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

Module 04 - Social Learning

	Run	View
Tutorial: Prosocial Learning
Exercise

• 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