Rick Thomas wins Intelligence Advanced Research Projects Agency Grant

Rick Thomas was recently awarded a grant from the Department of the Interior (DoI) Intelligence Advanced Research Projects Agency (IARPA) for Forecasting Counterfactuals in Uncontrolled Settings (FOCUS) for the proposal “Reasoning About Multiple Paths and Alternatives to Generate Effective Counterfactual Forecasts and Lessons Learned (RAMPAGE).”

Rick will be working in partnership with Charles River Analytics, Betty Whitaker (GTRI), Michael Dougherty (University of Maryland), and researchers at Michigan Tech.

Ashley Lawrence becomes Dr. Ashley Lawrence

Congratulations to Ashley Lawrence for successfully defending her dissertation: An Accessibility Framework for Cue-Based Inferences!

Many studies throughout the area of decision-making have shown that people are able to adapt to different decision environments. A number of frameworks have been proposed that seek to explain adaptive decision making in the context of cue-based inferences, a type of decision where a person decides which option is highest on a variable of interest based on the attributes of those options. However, current frameworks fail to account for the role of memory in cue-based inferences. The goal of this dissertation was to test whether a framework based on the accessibility of cues in memory can provide a better account of adaptive decision-making in cue-based inferences compared to either the adaptive toolbox or current single-strategy models. Three experiments were conducted to test the accessibility framework by manipulating decision environments as well as directly manipulating memory for cues. The results of the experiments extend previous research showing that memory affects cue-based inferences, challenging frameworks that are based on validity only. They also extend research on adaptive decision-making by showing that people are sensitive to the decision environment but that this does not always result in changes to both decision outcomes and decision processes. Overall, the accessibility framework provides a promising foundation for explaining how people make cue-based inferences, but further research is necessary to better understand how people search cues, particularly how they decide to stop searching.

Carolyn Hartzell defends master’s project

Congratulations to Carolyn Hartzell for successfully defending her master’s project!

Satisfaction of search errors, also called subsequent search misses, are a costly visual search problem, particularly in radiology. To date, research on causes and interventions for satisfaction of search errors has focused on properties of the stimuli and the mechanics of the search process. I present evidence to support a new theoretical understanding of some of the underlying cognition that drives search behavior and that can predict visual search errors. An eye-tracked experiment that manipulated participant expectations of target characteristics and number of targets demonstrated that participant expectations, generated based on environmental cues and long-term memory, influence search behavior. Through exemplar training, participants learned to associate cues with target sets that varied in color of target and number of targets. Participants were instructed to utilize these learned relationships to facilitate their visual search. Analysis of response time, fixation data, and miss errors indicated that expectation was a significant predictor of search behavior, with lower expectations for secondary targets being associated with shorter response times, more miss errors, and fewer fixations to unexpected colors. In a first step towards understanding the cognitive mechanisms behind visual search misses for secondary targets, a cognitive process model was developed. This model integrated hypothesis-guided search with visual search to predict participant behavior. The model was tested against the empirical data and successfully captured the high-level results of the experiment. Future iterations of the model will seek to better fit the more subtle complexities of the empirical results.