Recent research has shown that when people multitask, boththe subtask structure and the temporal constraints of thecomponent tasks strongly influence people’s task-switchingbehavior. In this paper, we propose an integrated theoreticalaccount and associated computational model that aims toquantify how people balance structural and temporalconstraints in everyday multitasking. We validate the theoryusing data from an empirical study in which drivers performeda visual-search task while navigating a driving environment.Through examination of illustrative protocols from the modeland human drivers as well as the overall fit on the aggregateglance data, we explore the implications of the theory andmodel for time-critical multitasking domains.

In using eye movements to develop cognitive models, researchers typically analyze eye movement protocols with aggregate measures and test models with respect to these measures. Because aggregate analyses sometimes conceal informative low-level behavior, protocol analyses comparing model predictions to individual trial protocols are frequently desirable; however, protocol analysis for eye movement data is often tedious and time-consuming. We describe how to automate the protocol analysis of eye movements using hidden Markov models. Working with data from an equation-solving task, we demonstrate two methods of tracing eye movement data—that is, mapping eye movements to the sequential predictions of a cognitive process model. W e evaluated these tracing methods in an experiment where participants were instructed to execute given equation-solving strategies. When coding the experimental protocols in terms of the given strategies, the automated tracing methods performed as well as human expert coders in a fraction of the time.