Learning from experience is a critical tool for how humans successfully predict and adjust to their changing world, whether a teacher trying to figure out which pedagogical technique will work best for a class, a doctor trying to figure out which medicine will work best for patient, or a business person trying to figure out which marketing strategy will produce the largest sales. The goal of this project is to better understand how people learn cause-effect relations and make causal judgments in a highly complex world. This will facilitate predicting when humans are likely to make good decisions and when humans are likely to make bad decisions, which can be costly for the individual and for society.
The research on how people learn causal relations has primarily focused on learning in independent and identically distributed, cross-sectional situations. However, humans also learn causal relations among events that are distributed in time, which often involves non-stationary and autocorrelated information. The first goal of this work is to identify processes people use to learn causal relationships in these longitudinal situations. The second goal is to identify people's ability to employ different learning strategies adaptively for both longitudinal and cross-sectional environments. The results will also be used to develop online simulations to help students learn how to make good causal judgments when designing and analyzing research.
