Programming for Cognitive and Brain Sciences

PDF version of this document

https://media.readthedocs.org/pdf/pcbs/latest/pcbs.pdf

Git repository on github (contains slides, solutions to exercices, and more)

https://github.com/chrplr/PCBS

Self-evaluation quizz (do you need to attend PROG101?):

https://forms.gle/iq7CN41DZMA6XJC59

Contents:

• 1. Foreword
• 2. Software Installation
  • 2.1. Instructions for Windows (with WSL)
  • 2.2. Instructions for native Windows (without WSL)
  • 2.3. Instructions for MacOS X
  • 2.4. Instructions for Ubuntu Linux
• 3. Check your installation
  • 3.1. Check R and Rstudio
  • 3.2. Check if Python can be executed in a Terminal
  • 3.3. Check Git
  • 3.4. Check Python
  • 3.5. Check basic graphics
  • 3.6. Check matplotlib
  • 3.7. Check pygame
  • 3.8. Check Expyriment
  • 3.9. Sublime Text
  • 3.10. Appendices
  • 3.11. Keep your local copy of the course material up to date
  • 3.12. Basic surviving skill: how to enter command lines in a Terminal
• 4. Starting from Scratch
  • 4.1. First steps
  • 4.2. Concepts learned so far
  • 4.3. Loops
  • 4.4. Two sprites
  • 4.5. Conditional execution or branching
  • 4.6. First series of exercices
  • 4.7. Variables
  • 4.8. Second series of exercices
• 5. Fun programming language
• 6. Interacting with a computer (in a Nutshell)
  • 6.1. The shell
  • 6.2. Disks, Directories and files
  • 6.3. filenames, directory structure
  • 6.4. Working directory. Absolute pathnames vs. relative pathnames (..)
  • 6.5. What is the PATH?
  • 6.6. What is a library (or module/package)?
• 7. Running Python
  • 7.1. Running a python script from the command line
  • 7.2. Testing a short piece of python code
  • 7.3. Write code with a text editor (Edit-run cycle)
  • 7.4. Using an Integrated Development Environment (IDE)
  • 7.5. Perform an interactive data analysis with jupyter-notebook or jupyter-lab
  • 7.6. Developping in Python with Emacs
• 8. Coding Exercises
  • 8.1. Flow control
  • 8.2. Lists
  • 8.3. Functions
  • 8.4. Strings
  • 8.5. Dictionaries
  • 8.6. File reading and writing
• 9. Automata and Computers
  • 9.1. The Computational Theory of Mind
  • 9.2. What is computation anyway ?
  • 9.3. The ancestors of the computer: the automata
  • 9.4. Formal description of an automaton
  • 9.5. Examples of transition diagrams
  • 9.6. What is a Computer?
  • 9.7. Register machines
  • 9.8. The Seven secrets of computers revealed
  • 9.9. Programmable computers
  • 9.10. Compilation and interpretation
  • 9.11. Operating systems
  • 9.12. What is a Terminal?
• 10. Representations of numbers, text, images
  • 10.1. Representation of integers
  • 10.2. Representation of text
  • 10.3. Representation of images
• 11. Creating stimuli
  • 11.1. Static visual stimuli
  • 11.2. Dynamic visual stimuli
  • 11.3. Creating and playing sounds
  • 11.4. More illusions
• 12. Experiments
  • 12.1. Simple reaction times
  • 12.2. Decision times
  • 12.3. Numerical distance effect
  • 12.4. Lexical Decision
  • 12.5. Posner’s attentional cueing task
  • 12.6. Stroop Effect
  • 12.7. A general audio visual stimulus presentation script
  • 12.8. Sound-picture matching using a touchscreen
  • 12.9. More examples using Expyriment
• 13. Programming a Lexical decision task
  • 13.1. Step 1: stimuli in constants
  • 13.2. Step 2: read stimuli from a csv file
  • 13.3. Select words in a lexical dabatase
  • 13.4. Automatising database searches with R and Python
  • 13.5. Generate nonwords
  • 13.6. Create a stimuli file
  • 13.7. Use sys.argv to pass the name of the file containing the list of stimuli
  • 13.8. Improving the pseudowords
  • 13.9. Data analysis
  • 13.10. Finally
• 14. Data Analyses
  • 14.1. Permutation tests
  • 14.2. Bootstrap
  • 14.3. Basic Data Analysis with R
  • 14.4. Comparing means using Easy ANOVA (Analysis of Variance)
  • 14.5. Frequency Analysis
• 15. Lexical Statistics
  • 15.1. Zipf law
  • 15.2. Benford’s law
  • 15.3. Neuroimaging
• 16. Online experiments
  • 16.1. HTML Basics
  • 16.2. Simple shapes
  • 16.3. Combining shapes
  • 16.4. Combining shapes with JS
  • 16.5. Using JsPsych
• 17. Regular Expressions
  • 17.1. Tutorial
  • 17.2. Example in Cognitive Science research
  • 17.3. More on the Theory
• 18. Simulations
  • 18.1. Monte Carlo Estimation
  • 18.2. Fractals
  • 18.3. Formal systems
  • 18.4. Artificial Neural networks
• 19. Hopfield Networks
  • 19.1. Model
  • 19.2. Computing the weight matrix
  • 19.3. Encoding images
  • 19.4. Simulations
  • 19.5. Discussion
• 20. Web Scraping
• 21. Tools to do Reproducible Science
  • 21.1. Two tools: literate programming and version control
  • 21.2. Lesson 101: how to compare files or directories
  • 21.3. Introducing git
  • 21.4. Working with remotes
• 22. Resources to learn Git
• 23. How to solve problems
  • 23.1. How to think like a programmer: lessons in problem solving
  • 23.2. How to report bugs effectively
  • 23.3. How to solve it by Polya
  • 23.4. Computer Science Distilled
• 24. Writing clean code
  • 24.1. Names
  • 24.2. Functions
  • 24.3. Successive refinement
  • 24.4. General
  • 24.5. Comments
  • 24.6. Testing
• 25. Building abstractions with recursive functions and higher-order functions
  • 25.1. Recursive functions
  • 25.2. Higher-order functions
  • 25.3. Reference
• 26. Sending TTL triggers
  • 26.1. Parallel Port
  • 26.2. DLP-IO8-G
  • 26.3. Python
  • 26.4. Reading an input line
  • 26.5. Arduino
  • 26.6. Raspberry Pi
• 27. Cogmaster Lectures
  • 27.1. Course description
  • 27.2. Prerequisites
  • 27.3. Projects
• 28. Projects
• 29. Resources
  • 29.1. Learning Python
  • 29.2. Programming skills
  • 29.3. Books relevant to Cognitive and Brain Sciences Programming
  • 29.4. Stimulus/Experiment generation modules
  • 29.5. Data analyses, Statistics in Python
  • 29.6. Simulations