Overview: One of the longstanding tensions in cognitive science is the relationship between human cognition and machine learning. Can modern machine learning methods teach us anything interesting about the human mind? Does cognitive psychology have a role to play in guiding the development of artificial intelligence systems? Why are machine learning systems so much better than humans at chess, but abysmally bad at the Atari game Frostbite? The goal in this elective is to provide an introduction to the current state of the art in computational cognitive science. On the "minds" side, we'll discuss papers examining topics in human learning, reasoning, decision making and social cognition. On the "machines" side, we'll go through a very gentle introduction to programming in R and building computational models of human cognition. Although some of the material is technical, this isn't a computer science class, and you aren't assessed on your programming skills! The focus is on questions about whether - and how - the comparison between human and the machine learning tells us something useful about the mind.
The particular papers used in the discussions vary from year to year, though they're always in the general area of cognitive psychology. You can find the list of papers from the 2017 class below. The 2018 reading list will be similar, but won't be identical.