Areas of Study
Research Topics
- Human judgment and decision making
- Computational cognitive modeling
- Machine learning approaches to human cognition
- Bayesian methods for model-based inference
My research takes a joint experimental and computational modeling approach to study human judgment, decision making, and reasoning. I study how people make decisions when faced with multiple, complex alternatives and options involving different risks and rewards. To address these questions, I develop probabilistic and dynamic models that can explain behavior and use hierarchical Bayesian methods for data analysis and model-based inference. I am also interested in combining machine learning techniques with cognitive models to study naturalistic human decision making. Recent research topics in my lab include understanding (1) how context affects multialternative, multiattribute choice, (2) how dynamically changing information impacts decision processes, and (3) how physicians make decisions from medical images.
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.
Research
- Combining Cognitive Modeling and Machine Learning to Understand Naturalistic Human Decision-making
- Multi-alternative, Multi-attribute Decision-making
- Dynamically Changing Information
- Medical Decision-making
- Quantum Cognition
