EPSCoR Research Fellows: NSF: Modeling Human Preparation and Anticipation Processes

To improve the information processing of a sudden event, human’s cognitive system can utilize various preceding signals for preparation. This ability is widely involved in activities requiring instant responses and decision making such as air and ground traffic, and operations of machinery, but existing decision-making models fall short in accounting for the complexities of preparation processes.

The project will provide a fellowship to an Assistant Professor at the University of Idaho and support a six-month trip to The Ohio State University (OSU), where the PI will receive specialized training from Dr. Roger Ratcliff about the Diffusion Decision Model (DDM) and attend advanced workshops on computational modeling. This project will leverage the expertise of both research collaborators to develop an integrated model of preparation effects on choice reactions.

This integrated model will enhance the understanding of human preparation and improve system designs by emphasizing the sequence and timing of operations. This project will improve the current measures of human attention, leading to more precise evaluations of people’s cognitive ability. The expertise in computational modeling will transform the PI's basic research skills and open pathways of interdisciplinary collaborations.

Through work with undergraduate and graduate students, the proposed project will also contribute to the STEM workforce across Idaho.

To improve the information processing of a sudden event, human’s cognitive system can utilize various preceding signals for preparation. Previous research on human preparation has deficits. First, models of preparation only predict the preparation effects on the overall speed of reactions.

Factors related to cognitive control and information processing are not included. Second, previous research on the interaction between preparation and cognitive control focused on providing empirical evidence instead of a computational framework. Third, the Diffusion Decision Model (DDM), the major computational model for explaining cognitive control phenomena in choice reactions, does not have processing assumptions that account for preparation effects.

Fourth, the current project aims to develop an integrated model of preparation effects on choice reactions from the formalized multiple trace theory of temporal preparation (fMTP) and DDM to explain the mechanism behind preparation and the interaction between preparation and cognitive control. The new model will be the first to integrate preparation factors with traditional choice reaction models.

Predictions of the new model will improve future designs of choice reaction experiments by providing precise estimates of different temporal impacts. The new model will explain the preparation effect on the overall reaction speed and its interaction with cognitive control. This will motivate theoretical development on human preparation and help integrate the areas of phasic alertness and temporal preparation.

Finally, the new model will predict human performance in instant decision-making scenarios more precisely by considering the impact of preparation, which will improve the evaluation of people’s cognitive ability.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.