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)

2016 course content

The first year for this class was 2016, and the topics focused on categorisation, reasoning and decision-making. The reading list for this one seems to have disappeared in my archives but the structure was largely the same as 2017: