| Description |
Research concerning human performance in complex multitask environments relies heavily upon the fundamental psychological principles of limited-capacity attention and top-down mechanisms of attention allocation. To provide a suitable computational model for limited attention on a measure of cognitive workload, we implement a hierarchical Bayesian evidence accumulation framework for a discrete/continuous detection task (Experiment 1) and two simultaneous choice tasks (Experiment 2). We measure fluctuations in cognitive workload for instruction-induced task priority for a standard measure of cognitive workload in driving and a steering task (Experiment 1) and simultaneous decisions of a computer-based task (Experiment 2). Evidence accumulation modeling provides evidence for changes in both information processing speed (drift rate) and certainty of responses (response threshold). The results indicate that both drift rate and response threshold vary with processing priority, with a greater contribution from response thresholds. The most robust finding suggests that-contrary to strictly resourcelimited theories of attention-strategic allocation of resources can drive performance more than a dynamic slowing in the rate of information processing. |
