James Anderson
Steven Sloman
David Sobel
Kathryn Spoehr

How do we make decisions and generate new inferences? How do we represent causal and mathematical knowledge? How is causal knowledge learned? Research in Judgment, Decision Making, and Learning in the department emphasizes the role that different computational and mental models play in reasoning and the acquisition of knowledge.

Anderson's research examines the role that neural networks can play in understanding mathematical reasoning. He examines whether these networks can represent and perform arithmetic functions on numbers. This research also raises questions about the way networks can be designed to perform mathematical operations.

Sloman examines how people reason about cause and effect, and its relations to understanding counterfactuals and interventions. His recent work examines the viability of formal probabilistic models of causal inference. Sloman also studies judgments of confidence and probability, whether human judgments are coherent and have a rational basis.

Sobel explores the nature of children's and adult's causal inferences. His research suggests that young children have sophisticated inferential machinery for making inferences based on ambiguous data, and learning causal structure from minimal information. He is currently exploring computational models that can accurately account for these abilities.

Spoehr's area of expertise is in human problem solving and reasoning, as well as the acquisition of cognitive skills from computer-based systems. She examines how technology can be used to improve teaching and learning in high schools. She has also investigated what cognitive mechanisms underlie the acquisition of expertise in complex conceptual domains.

Students interested in this topic area are recommended to take introductory courses in human cognition (CG42) making decisions (CG50), and cognitive development (CG63). Advanced courses include a seminar on reasoning and decision making (CG188), a course on the study of thinking (CG152), and the Cognition lab class (CG153). The department also offers related courses in cognitive development (CG118), the relation between perception and action (CG138) and courses in Machine learning (CG171. CS141, CS148)