Beginner’s CoCoSci list

  • I’ll come back to add comments on why I think these books, websites, lists, etc. are amazing when I get the chance.
  • Also, I’ll keep updating as I know or think of more.



  1. Probability Theory: The Logic of Science (E. T. Jaynes, 2003)
    • Above is THE book that sets the foundation for modern Bayesian probability theory. More exiting still for cognitive scientists, it is not just about how mathematicians make sense of data or scientists make discoveries, but also about how the human mind makes sense of the world in an intuitive way. A must-read if you love “math on the mind”.
    • Link: Amazon
  2. Bayes’ Rule: A Tutorial Introduction to Bayesian Analysis (J. V. Stone, starting 2013)
  3. Information Theory: A Tutorial Introduction (J. V. Stone, 2015)
  4. A Mathematical Primer for Social Statistics (J. Fox, 2009)
    • A quick (and very readable) refresher of linear algebra and calculus, both of which are essential for understanding stats and building computational models. Highly recommend if you want to recover a reasonable working knowledge of math without going through 1000-page linear algebra and calculus textbooks for math majors (again).
    • Link: Amazon, Fox’s website 


  1. Computational Modeling in Cognition: Principles and Practice (Lewandowsky & Farrell, 2010)
  2. Bayesian Cognitive Modeling: A Practical Course (Michael Lee, 2014)
  3. Artificial Intelligence: A Modern Approach (3rd Edition) (Russell & Norvig, 2009)
    1. Link: Amazon, Berkeley website, GitHub
    2. Language: Python, LISP, Julia, Scala, Java, C#, Javascript
  4. The Cambridge Handbook of Computational Psychology (Sun, 2008)
  5. Probabilistic Models of Cognition (Goodman & Tenenbaum, online book)
  6. Statistical Rethinking: A Bayesian Course with Examples in R and Stan (McElreath , 2015)
  7. Foundational papers


  1. MATLAB/Octave
  2. R
  3. Python
  4. Church
  5. (formatting) LaTeX

Online Experiments

  1. MTurk
  2. psiTurk

Modeling + Cognitive Development

  1. Rational Constructivism in Cognitive Development (Xu & Kushnir, 2012)
  2. Causal Learning: Psychology, Philosophy, and Computation (Gopnik & Schulz, 2007)

Popular Science

  1. Algorithms to Live By (Christian & Griffiths, 2016)
  2. Thinking, Fast and Slow (Kahneman, 2013)

Reading lists, resources, blogs…


  1. Josh Tenenbaum (MIT): resources
  2. Tom Griffiths (UC Berkeley): reading list, big data 
  3. Amy Perfors (University of Adelaide): general resources, course
  4. Dan Navarro (UNSW): resources 
  5. Noah Goodman (Stanford): resources
  6. Mike Frank (Stanford): past syllabi, blog
  7. Todd Gureckis (NYU): resources, blog
  8. Robert Jacobs (Rochester): Computational Cognition Cheat Sheets
  9. Garrison Cottrell (UCSD): Cognitive Modeling Greatest Hits, resources
  10. Rebecca Saxe (MIT): Theory of Mind resources
  11. Andreas Stuhlmüller (MIT): Ought, personal website
  12. Sharon Goldwater (University of  Edinburgh): reading list
  13. ESSLLI summer school: 2016 (Composition in Probabilistic Language Understanding), 2014 (Probabilistic Programming Languages)
  14. Brendan O’Connor (UMass): AI and social science
  15. Monica Gates (UC Berkeley): science outreach
  16. Jessica Hamrick (UC Berkeley): qual reading notes
  17. Wai Keen Vong (Rutgers): blog
  18. Baxter Eaves (Rutgers): blog

Cognitive Development

  1. Samuel G. B. Johnson (Yale): research

Stats & Methodology

  1. Daniël Lakens  (Eindhoven University of Technology): blog (the 20% statistician), personal 
  2. Sanjay Srivastava (University of Oregon): blog (the hardest science, e.g., everything is fucked)
  3. Will Gervais (University of Kentucky): stats books
  4. Simine Vazire (UC Davis): blog (sometime i’m wrong)
  5. Brian Nosek (Virginia): open science
  6. Ed Vul (UCSD): “voodoo correlation” (paper, book chapter)
  7. John Kruschke (Indiana University): blog (doing Bayesian data analysis)


  1. Lewandowsky and Ecker (UWA): research tools
  2. Brad Voytek (UCSD): lab philosophy
  3. Mike Pacer (UC Berkeley): qualifying exams
  4. The Professor Is In
    • Advice on how to build a career out of Ph.D., inside or outside the academia.
  5. Konrad Kording (Northwestern): resources (e.g., data skills, writing, productivity)
  6. Dredze (JHU) and Wallach (UMass): how to be a successful PhD student
  7. Matt Might (Utah): blog
  8. Tim Brady (UCSD): MTurk, journal ranking, related references
  9. Brian Scholl (Yale): musings


  1. Jordan Suchow (UC Berkeley): reading list
  2. Falk Lieder (UC Berkeley): practical rationality
  3. Monica Gates (UC Berkeley): blog
  4. Jessica Hamrick (UC Berkeley): blog
  5. Robert Hawkins (Stanford): website

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