Jennifer Trueblood

Jennifer Trueblood's research takes a joint experimental and computational modeling approach to study human judgment, decision-making, reasoning, and memory. She is interested in understanding (1) how people make decisions when faced with multiple alternatives, (2) how dynamically changing information affects decision processes, (3) how people reason about complex causal events, and (4) how different perspectives, contexts, and frames can lead to interference effects in decision-making and memory. To address these questions, she develops probabilistic and dynamic models that can explain behavior and uses hierarchical Bayesian methods for data analysis and model-based inference.