Abstract

We test people's ability to optimize performance across two concurrent tasks. Participants performed a number entry task while controlling a randomly moving cursor with a joystick. Participants received explicit feedback on their performance on these tasks in the form of a single combined score. This payoff function was varied between conditions to change the value of one task relative to the other. We found that participants adapted their strategy for interleaving the two tasks, by varying how long they spent on one task before switching to the other, in order to achieve the near maximum payoff available in each condition. In a second experiment, we show that this behavior is learned quickly (within 2–3 min over several discrete trials) and remained stable for as long as the payoff function did not change. The results of this work show that people are adaptive and flexible in how they prioritize and allocate attention in a dual‐task setting. However, it also demonstrates some of the limits regarding people's ability to optimize payoff functions.

Highlights

  • With the growing ubiquity of mobile technology, people regularly interleave attention between concurrent tasks

  • We investigate how priorities, as formalized through an explicit payoff function, affect how people choose to allocate their attention when multitasking

  • We investigate human multitasking behavior by focusing on how people decide to interleave two concurrent tasks

Read more

Summary

Introduction

With the growing ubiquity of mobile technology, people regularly interleave attention between concurrent tasks. Human multitasking occurs in a variety of task domains and is. When people are faced with two or more tasks, they are faced with a scheduling problem: deciding how much time to spend on one task before shifting attention to the task (Moray, Dessouky, Kijowski, & Adapathya, 1991). Given the prevalence of multitasking, it is important to understand how well people deal with the problem of allocating their attention between tasks

Objectives
Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call