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Computational modeling

Reinforcement Learning: An Introduction, by Sutton & Barto and associated resources from Rich Sutton's website. The "RL Bible," if you will - a must-read.

Great set of papers collated by Yael Niv's lab on reinforcement learning and statistics.

Book chapter by Nathaniel Daw on an introduction to trial-by-trial cognitive computational modeling.

Step-by-step tutorial on standard RL models and Bayesian learning models in Matlab. From Jill O'Reilly and Hanneke den Ouden.

A collection of resources from Sam Gershman's lab, including an excellent suggested reading list and lots of example code for computational models.

Beautiful and simple visualizations of basic Bayesian inference theory/basic probability, from Daniel Kunin and colleagues.

Free resources and recordings for all lectures of Harvard's Statistics 110 (Probability) course, taught by Joe Blitzstein.

Mentorship, diversity, & equity

The Women in Psychology (WiP) group at Harvard, which I started as a grad student. Check out our "Resources" and "See the Stats" pages for information, and follow us on Twitter for announcements about our public events and workshops.

Tool to make sure your citations are gender balanced. From the Postle Lab/JoCN.

List of organizations/resources related to diversity in neuroscience. From Bias Watch Neuro, a group focused on gender balance at neuroscience conferences.

Wonderful set of resources/research from the NAS on effective mentoring practices.

Writing & professional development

Free copy of Barbara Sarnecka's book (and associated resources), The Writing Workshop: Write More, Write Better, Be Happier in Academia. I highly recommend being a part of/forming your own local writing group as outlined in Sarnecka's book.

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