Strategic Adaptation to Task Characteristics, Incentives, and Individual Differences in Dual-Tasking

Abstract

We investigate how good people are at multitasking by comparing behavior to a prediction of the optimal strategy for dividing attention between two concurrent tasks. In our experiment, 24 participants had to interleave entering digits on a keyboard with controlling a randomly moving cursor with a joystick. The difficulty of the tracking task was systematically varied as a within-subjects factor. Participants were also exposed to different explicit reward functions that varied the relative importance of the tracking task relative to the typing task (between-subjects). Results demonstrate that these changes in task characteristics and monetary incentives, together with individual differences in typing ability, influenced how participants choose to interleave tasks. This change in strategy then affected their performance on each task. A computational cognitive model was used to predict performance for a wide set of alternative strategies for how participants might have possibly interleaved tasks. This allowed for predictions of optimal performance to be derived, given the constraints placed on performance by the task and cognition. A comparison of human behavior with the predicted optimal strategy shows that participants behaved near optimally. Our findings have implications for the design and evaluation of technology for multitasking situations, as consideration should be given to the characteristics of the task, but also to how different users might use technology depending on their individual characteristics and their priorities.

Global difficulty modulates the prioritization strategy in multitasking situations

“…These results are in line with a significant body of work demon strating the importance of prioritization to drive attentional strategy (Anderson, 2013;Chelazzi et al, 2014;Gopher et al, 2000;Gopher and Brickner, 1982;Janssen and Brumby, 2015;Kurzban et al, 2013;Matton et al, 2016;Schumacher et al, 2001), and more generally in human regulation (Loft et al, 2007). More particularly, it shows that resources allocation is prone to top down modulations, which may take precedence over a "greedy and polite" resources allocation policy.…”

Strategic predictors of performance in a divided attention task

“…Timing or prioritizing tasks is another aspect that affects performance in multitasking settings [ 24 , 33 , 42 ]. Task preference can be influenced through altering payoffs [ 33 , 43 ]. To maximize performance when performing multiple tasks in parallel, interleaving between them is essential [ 43 – 45 ].…”

“…Task preference can be influenced through altering payoffs [ 33 , 43 ]. To maximize performance when performing multiple tasks in parallel, interleaving between them is essential [ 43 – 45 ]. However, this leads to interruptions [ 45 , 46 ], which can have negative effects on achievement and also represent an issue from a cognitive modeling perspective [ 47 ].…”

“…The attention interleaving strategies revealed that in order to maximize performance in the driving task, subjects chose to return their attention to steering before the natural subtask boundary in dialing. In a more recent study [ 43 ], using a similar typing-while-tracking dual-task, the authors found that interleaving strategy was influenced by change in task characteristics and monetary incentives, together with individual differences in typing ability. A computational modeling analysis assessed in both studies suggested that people adapt their strategies to meet specific performance objectives and to maximize payoff.…”

Games for Psychological Science

“…()'s findings reveal the surprising individual variation in highly practiced skill. This is interesting because it further exposes the difficulty of building psychological theory in the face of the extreme strategy flexibility of humans (Howes, Lewis, & Vera, ; Janssen & Brumby, ; Tseng & Howes, ). Even when behavior is heavily constrained by a well‐defined environment, explicit objective functions, and many hundreds of hours of practice, there is still substantial individual variation in strategy.…”