The goal of the project is to better understand the causes and consequences of cognitive load in complex task environments. We employ behavioral, subjective, and physiological (EEG, heart rate, and eye movement) methods to assess an individual’s workload level and we use models based in cognitive architectures to gain insights into the specific cognitive capacities involved (memory, perceptual processing, motor planning, etc.;  Stevens et al., 2019). We are also interested in understanding the cognitive events that drive subjective feelings of workload and metacognitive decisions about workload and effort. By identifying these capacities, we can develop targeted interventions that address potential causes of high workload. The project will involve analysis of behavioral and physiological data to identify patterns in related to workload conditions, expanding on an existing cognitive model to improve predictions, and/or reviewing relevant literature.

Stevens C.A., Fisher C.R., Morris M.B., Myers C., Spriggs S., Dukes A. (2019) Cognitive Metrics Profiling: A Model-Driven Approach to Predicting and Classifying Workload. In: Cassenti D. (eds) Advances in Human Factors in Simulation and Modeling. AHFE 2018. Advances in Intelligent Systems and Computing, vol 780. Springer, Cham

key words

cognitive workload; cognitive models; metacognition; physiological metrics; multitasking

Citizenship:  Open to U.S. citizens

Level:  Open to Postdoctoral and Senior applicants