Paper Reading #27: Sensing cognitive multitasking for a brain-based adaptive user interface

Reference Information
Sensing cognitive multitasking for a brain-based adaptive user interface by Erin Treacy Solov, Francine Lalooses, Krysta Chauncey, Douglas Weaver, Margarita Parasi, Matthias Scheutz, Angelo Sassaroli, Sergio Fantini, Paul Schermerhorn, Audrey Girouard, Robert J.K. Jacob
Author Bios

• Erin Treacy Solov is a postdoctoral fellow in the Humans and Automation Lab (HAL) at MIT. 
• Francine Laloosesis a PhD candidate at Tufts University and has a Bachelor's and Master's degree from Boston University
• Krysta Chauncey is a post doctorate researcher at Tufts University
• Douglas Weaver has a doctorate degree from Tufts University
• Margarita Parasi is working on a Master's degree at Tufts University
• Angelo Sassaroli is a research assistant professor at Tufts University and has a PhD from the University of Electro-Communication
• Sergio Fantini is a professor at Tufts University in the Biomedical Engineering Department
• Paul Schermerhorn is a post doctorate researcher at Tufts University and has studied at Indiana University
• Audrey Girouard is an assistant professor at The Queen's University and has a PhD from Tufts University
• Robert J.K. Jacob is a professor at Tufts University

Summary
Hypothesis
Cognitive multitasking is a common element in daily life, and the researchers' human-robot system can be useful in recognizing these multitasking tasks and assisting with their execution.

Methods
The users were intermediately comfortable with computing. The phones ran Android 2.1. The built-in hard buttons were disabled for the test. Eye gaze data was recorded to determine eye movement start and stop time, with the display and phone separately sufficiently to identify eye movements. To simulate expert usage, icons appeared to reveal the needed gesture or command. Feedback was immediately related to the user.

The authors used a repeated-measures within-participants experimental design. Users completed a questionnaire. Then, they completed each of the 12 commands six times per environment. The condition order was counterbalanced through randomization. The variables considered were completion time, mode errors, command errors, and baseline and concurrent distractor task performance. Gestures were recorded. The users then completed another questionnaire.

Results
1) The preliminary study showed to be accurate enough for the researchers to continue.
2) A significant difference in response time was found between delay and the other two conditions but no difference was found between the other two. They also found that the 3 conditions had very distinct hemodynamic responses and could be measured that way.
3) No significantly different results were found for response time or accuracy.

Contents
The 3 scenarios of multitasking are:

• Branching - Task switching while keeping secondary task in memory
• Dual-Task - Frequent changes in task that do not require memory
• Delay - Secondary task can wait for primary task to finish

The 2 conditions of branching are:

• Random Branching - User does not expect task
• Predictive Branching - User expects task

Discussion
I think the researchers needed a larger test base to be able to verify their results as significant.