A Cognitive Model of the Effects of Workload on Perceptual Span

More accurately understanding how individuals deploy attention in multitasking environments helps us develop models that more accurately capture human performance and variability. Here, we implemented a method of measuring subjective workload in an ACT-R model and constrained the model's ability to use bottom-up capture for stimuli outside of a peripheral window (i.e., perceptual span). Stimuli outside of the perceptual span window could thus only be detected via top-down attention. Our subjective workload metric was based on event-frequency and was compared to NASA-TLX reports from multitasking data using the AF-MATB in \citeA{bowers2014effects}. The metric successfully differentiated between Easy and Hard task demands. We then evaluated performance and eye movements of an ACT-R model with different fixed levels of perceptual span. As expected, when the model was limited to mostly top-down visual attention, performance declined because the model could not directly attend to malfunctions in peripheral vision. Similarly, saccade amplitude decreased and eye movements became more systematic. Interestingly, when comparing the model's simulation to behavioral data, the size of the perceptual span window increased as task demands increased, suggesting that participants were using less systematic scans when subjective workload increased. We then implemented this transition in the ACT-R model.