Minds and Machines


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 not so great 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. (back to current year)


2017 course content

In 2017, this class focused on two core topics: "Probabilistic reasoning by children" and "Learning from people", with 4-5 papers on both topics as well as a few general introduction papers. Content for the class: