Exploring multitasking strategies in an ACT-R model of a complex piloting task

Multitasking is a challenging cognitive task, and there are many factors driving which strategy participants use to complete tasks concurrently. We utilized a model comparison approach to evaluate how participants decide which task to switch to next using the Air Force Multiple Attribute Battery (AF-MATB). We used the cognitive architecture, Adaptive Control of Thought – Rational (ACT-R), to simulate multitasking in the AF-MATB. We varied how the model decided which task to attend to next by comparing a purely top-down strategy, a purely reactive, bottom-up selection strategy, and mixtures of the two. We compared simulations of the model to data from Bowers et al., (2014). The best combination involved a mixture of top-down and bottom-up selection. Neither the purely top-down nor bottom-up selection models performed well. These results suggest that participants use a complex mixture of strategies to multitasking. The use of a top-down strategy suggests participants could develop efficient strategies to multitask successfully, and that participants may be using a more effortful serial search for tasks, as indicated by the model's serial processing implementation.