Programming Psychology Experiments

Objectives

• learn to build experimental psychology experiments, that is, how to create visual and auditory stimuli, to present them at precise times and possibly record participants’ responses and analyse them

• learn tools to do reproducible science (github, markdown, …)

• improve your programming skills (clean code, real-time, …)

Pedagogy

• The lectures essentially consist of Practical tutorials: After we introduce a topic, you have to solve challenges (with help from us if necessary).

• We will use mostly Python, but also Javascript for online experiments.

• Bring your computer and have the software installed in advance !!!

Expectations/Evaluations

• Attendance is expected (warn me by email if you cannot attend for a good reason).

• Working during the course is also expected. At the end of lectures, I will asked to you to send me by email a copy of your work folder, with your attempts.

Resources:

Our Github repositories:

• https://github.com/chrplr/programming-psychology-experiments

• https://github.com/chrplr/PCBS

Python

• Books:

  • Automate the boring stuff with Python https://automatetheboringstuff.com/ (highly recommended!)

  • Apprendre à Programmer avec Python3 https://inforef.be/swi/python.htm

  • Think Python http://greenteapress.com/thinkpython2/

• MOOCs:

  • Udemy’s Python programming for absolute beginners https://www.udemy.com/python-programming-for-absolute-beginners/

  • Code Academy’s Learn Python module https://www.codecademy.com/learn/learn-python

  • Openclassrooms’ Apprendre à programmer en Python https://openclassrooms.com/fr/courses/235344-apprenez-a-programmer-en-python

  • Python 3 : des fondamentaux aux concepts avancés du langage https://www.fun-mooc.fr/en/cours/python-3-des-fondamentaux-aux-concepts-avances-du-langage/

Programming skills

• Software Carpentry https://software-carpentry.org/lessons/ provides nice lessons about writing software for science and do reproducible science.

Unix Command line (shell)

• The Linux Command Line http://linuxcommand.org/tlcl.php by Williams Shotts.

• Openclassrooms MOOC https://openclassrooms.com/courses/reprenez-le-controle-a-l-aide-de-linux

Git

• Openclassrooms’ MOOC Manage your code with Git and Github https://openclassrooms.com/en/courses/5671626-manage-your-code-project-with-git-github

• https://product.hubspot.com/blog/git-and-github-tutorial-for-beginners

• https://git-scm.com/book/en/v2/Getting-Started-Git-Basics

• The Git Book https://git-scm.com/book/en/v2

• My own git cheat page http://www.pallier.org/version-control-at-your-fingertips-a-quick-start-with-git.html#version-control-at-your-fingertips-a-quick-start-with-git

Cognitive and Brain Sciences Programming

• Programming Visual Illusions for Everyone https://www.programmingvisualillusionsforeveryone.online/ by Marco Bertamini.

• Neural Data Science: A Primer with MATLAB and Python by von Erik Lee Nylen and Pascal Wallisch

• Matlab for Brain and Cognitive Scientists and Analyzing neural time series data by Mike X Cohen

• Python in Neuroscience https://www.frontiersin.org/research-topics/8/python-in-neuroscience

• Modeling Psychophysical Data in R by Kenneth Knoblauch & Laurence T. Maloney

Stimulus/Experiment generation modules

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• http://www.expyriment.org (See Get started with Expyriment https://docs.expyriment.org/Tutorial.html

• http://psychopy.org (See Programming with PsychoPy https://www.socsci.ru.nl/wilberth/nocms/psychopy/print.php)

• http://psychtoolbox.org/ (See Psychtoolbox demos http://peterscarfe.com/ptbtutorials.html)

• https://www.jspsych.org/ (See intro at https://blog.s-m.ac/using-jspsych/)

• https://dialoguetoolkit.github.io/chattool/

Data analyses, Statistics in Python

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• Modules: numpy, scipy, pandas, seaborn, statsmodel, sklearn

  • Data manipulation:

    • http://pandas.pydata.org/pandas-docs/stable/tutorials.html

  • Plotting:

    • http://matplotlib.org/users/pyplot_tutorial.html

    • https://seaborn.pydata.org/tutorial.html

• Scipy Lecture Notes: http://www.scipy-lectures.org/

• Think Stats by Allen B. Downey: http://greenteapress.com/thinkstats2/

• Python Data Science Handbook by Jake VanderPlas: https://jakevdp.github.io/PythonDataScienceHandbook

• Introduction to Data Science in Python: notebook from a 2 day workshop organized by the Paris-Saclay Center for Data Science: https://github.com/paris-saclay-cds/data-science-workshop-2019

• Machine Learning with scikit-learn” MOOC: https://www.fun-mooc.fr/en/courses/machine-learning-python-scikit-learn/ (on github: https://inria.github.io/scikit-learn-mooc/)