- 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.

## Theory

- 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

- Bayes’ Rule: A Tutorial Introduction to Bayesian Analysis (J. V. Stone, starting 2013)
- Link: Amazon (basic, Python, R, MATLAB), Stone’s website

- Information Theory: A Tutorial Introduction (J. V. Stone, 2015)
- Link: Amazon (basic), Stone’s website (including MATLAB and Python code)

- 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

## Modeling

- Computational Modeling in Cognition: Principles and Practice (Lewandowsky & Farrell, 2010)
- Link: Amazon, Todd Gureckis’s course, Frank Schieber’s course
- Language: MATLAB

- Bayesian Cognitive Modeling: A Practical Course (Michael Lee, 2014)
- Link: Amazon, Lee’s website
- Language: WinBUGS (supported by R and MATLAB)

- Artificial Intelligence: A Modern Approach (3rd Edition) (Russell & Norvig, 2009)
- Link: Amazon, Berkeley website, GitHub
- Language: Python, LISP, Julia, Scala, Java, C#, Javascript

- The Cambridge Handbook of Computational Psychology (Sun, 2008)
- Link: Amazon

- Probabilistic Models of Cognition (Goodman & Tenenbaum, online book)
- Link: MIT website
- Language: Church

- Statistical Rethinking: A Bayesian Course with Examples in R and Stan (McElreath , 2015)
- Link: Amazon, McElreath’s website
- Language: R, Stan

- Foundational papers

## Programming

- MATLAB/Octave
- MATLAB for Behavioral Scientists (2nd Edition) (Rosenbaum, Vaughan, & Wyble, 2014)
- Machine Learning (Coursera course by Stanford’s Andrew Ng)

- R
- Learning Statistics Using R (Dan Navarro, starting 2011)
- Discovering Statistics Using R (Andy Field, 2012)
- An R Companion to Applied Regression (John Fox, 2011)
- R for Reproducible Scientific Analysis
- Introduction to Programming in R
- Learning R for Researchers in Psychology

- Python
- How to Think Like a Computer Scientist (online book)
- Python for Non-programmers (online book)

- Church
- (formatting) LaTeX

## Online Experiments

- MTurk
- psiTurk

## Modeling + Cognitive Development

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

## Popular Science

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

## Reading lists, resources, blogs…

#### CoCoSci

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

#### Cognitive Development

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

#### Stats & Methodology

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

#### Academia

- Lewandowsky and Ecker (UWA): research tools
- Brad Voytek (UCSD): lab philosophy
- Mike Pacer (UC Berkeley): qualifying exams
- The Professor Is In
- Advice on how to build a career out of Ph.D., inside or outside the academia.

- Konrad Kording (Northwestern): resources (e.g., data skills, writing, productivity)
- Dredze (JHU) and Wallach (UMass): how to be a successful PhD student
- Matt Might (Utah): blog
- Tim Brady (UCSD): MTurk, journal ranking, related references
- Brian Scholl (Yale): musings

#### Miscellaneous

- Jordan Suchow (UC Berkeley): reading list
- Falk Lieder (UC Berkeley): practical rationality
- Monica Gates (UC Berkeley): blog
- Jessica Hamrick (UC Berkeley): blog
- Robert Hawkins (Stanford): website