We use behavioral experiments and computational modeling to study how people think, judge, and decide. There are three interrelated components of our research program.
Behavior: The first involves an analysis of behavioral decision models. We study the relationships between different models, the effects of various noise specifications on their predictions, and the difficulties inherent in recovering model parameters from choice data. By doing so, we hope to provide cohesive quantitative insights regarding the fundamental properties of choice behavior, and shed light on the ways in which models of choice behavior can be appropriately tested and compared.
Process: The second component of our research program involves specifying the cognitive processes at play in behavior. We extend research on perceptual decision making, attention, and memory retrieval, to model information sampling and aggregation in domains such as multiattribute choice, risky choice, and probability judgment. We are particularly drawn to evidence accumulation models, which provide a psychologically, neurobiologically, and statistically grounded approach to specifying information aggregation processes in choice. We try to explain observed behavioral patterns as outcomes of the computational operations implemented by these models, and test our explanations using process data on response time, recall, and attention.
Representation: The final component of our research program involves specifying the mental representations that are sampled and aggregated as people make judgments and decisions. Here we apply methodological insights from semantic memory research and computational linguistics (particularly tools such as word embeddings) to uncover knowledge representations for objects, attributes, and events that are the focus of everyday judgment and decision tasks. By specifying both what people know about the world, and how they use this knowledge to form preferences and beliefs, we are able to build models that can deliberate over and respond to a large variety of everyday decision problems in a human-like manner.