Steven Sloman -- The Limits of Causal Reasoning in Human and Machine Learning

The Limits of Causal Reasoning in Human and Machine Learning
Prof Steven Sloman, Brown University

Abstract: A key purpose of causal reasoning by individuals and by collectives is to enhance action, to give humans yet more control over their environment. As a result, causal reasoning serves as the infrastructure of both thought and discourse. Humans represent causal systems accurately in some ways, but also show some systematic biases (we tend to neglect causal pathways other than the one we are thinking about). Even when accurate, people’s understanding of causal systems tends to be superficial; we depend on our communities for most of our causal knowledge and reasoning. Nevertheless, we are better causal reasoners than machines. Modern machine learners do not come close to matching human abilities.

Speaker bio: Steve did his PhD in Psychology at Stanford University from 1986-1990 and then did post-doctoral research for two years at the University of Michigan. He is the former Editor-in-Chief of the journal Cognition. Steven is a cognitive scientist who studies how people think. He has studied how our habits of thought influence the way we see the world, how the different systems that constitute thought interact to produce conclusions, conflict, and conversation, and how our construal of how the world works influences how we evaluate events and decide what actions to take.

http://www.educationalneuroscience.or...