noob 2 l33t: now with graphs

Myself and Mike Dewar have just had a paper published in the journal Psychological Science. In it we present an analysis of what affects how fast people learn, using data from over 850,000 people who played an online game called Axon (designed by our friends Preloaded. This is from the abstract:

In the present study, we analyzed data from a very large sample (N = 854,064) of players of an online game involving rapid perception, decision making, and motor responding. Use of game data allowed us to connect, for the first time, rich details of training history with measures of performance from participants engaged for a sustained amount of time in effortful practice. We showed that lawful relations exist between practice amount and subsequent performance, and between practice spacing and subsequent performance. Our methodology allowed an in situ confirmation of results long established in the experimental literature on skill acquisition. Additionally, we showed that greater initial variation in performance is linked to higher subsequent performance, a result we link to the exploration/exploitation trade-off from the computational framework of reinforcement learning.

The paper is behind a paywall for the next year, unfortunately, but you can find a pre-print, as well as all the raw data and analysis code (written in Python) in the github repo. I wrote something on my academic blog about the methods and why we wanted to make this an example of open science.

Links: The paper: Tracing the Trajectory of Skill Learning With a Very Large Sample of Online Game Players
And the data & code.

Thanks to @phooky for suggesting an alternative title for the paper, which I’ve used to title this post

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