Cognitive Workload Modeling of Task Priority in Detection and Choice Tasks
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Summary
PhD dissertation (University of Utah, 2019) using hierarchical Bayesian evidence-accumulation modeling to decompose cognitive workload during prioritized multitask performance. Experiment 1 paired a continuous steering task with the discrete Detection Response Task (DRT), a standard driving-workload measure, under instruction-induced priority manipulations; Experiment 2 paired two simultaneous choice tasks. Across experiments, both drift rate (information-processing speed) and response threshold (response certainty) varied with task priority, with response threshold contributing more strongly. The pattern argues against strict resource-limited theories: strategic resource allocation drives performance more than dynamic slowing of information uptake.
Key finding
Bayesian evidence-accumulation modeling of workload tasks shows priority-driven performance changes are dominated by shifts in response threshold rather than drift-rate slowing, undermining purely resource-limited accounts of attention.
Methodology
Two experiments + hierarchical Bayesian evidence-accumulation modeling (LBA-family)
Sample size: Two experiments (N reported per-experiment in dissertation)
Quality score: 5 / 5