Blog #27: Sensing Cognitive Multitasking for a Brain-Based Adaptive User Interface

Paper Title: Sensing Cognitive Multitasking for a Brain-Based Adaptive User Interface

Authors: Erin Solovey, Francine Lalooses, Krysta Chauncey, Douglas Weaver, Margarita Parasi, Matthias Scheutz, Angelo Sassaroli, Sergio Fantini, Paul Schermerhorn, Audrey Girouard and Robert Jacob

Author Bios:

Erin Solovey: is a postdoctoral fellow in the Humans and Automation Lab (HAL) at MIT

Francine Lalooses: is 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 Sasaroli: 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 Jacob: is a professor at Tufts University

Presentation Venue: CHI '11 Proceedings of the 2011 annual conference on Human factors in computing systems that took place at New York (ACM)

Summary:
Hypothesis: Cognitive miltitasking 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. If the authors can create a system to detect a user's "to-do list" and allow them to multitask on different things at once, then those tasks will be completed faster., the user will become "understood" by the system, and a new kind of technology will be effectively used.

How the hypothesis was tested: The first experiment was designed to highlight three conditions: delay, dual-task and branching. The participants interacted with a simulation of a robot on Mars, sorting rocks. Based on the pattern/order of rock classification, measure data related to each of the three conditions listed above.

The second experiment was used to determine whether they could distinguish specific variations of the branching task. Branching was divided into two categories: Random branching and predictive branching. Also, the experiment followed the same basic procedure as the first experiment. However, there were only two experimental conditions

Results: The preliminary study returned a recognition accuracy of 68%. To the authors, this was promising. In the second study with the robot and the rocks, any result where the participant achieved less than a score of 70% were discarded because it was seen as the task being done incorrectly. In the last study, there was no significant statistical difference found between the random and predictive branching. The authors were able to construct a proof-of-concept model because they were able to differentiate between the three types of tasking and incorporate machine learning to it.

Discussion:
Effectiveness: I found these guys very bright. The technology they use is super advanced and the methodologies were complex when dealing with the proof-of-concept system. I don't know how much effect this will have in the HCI field because it didn't seem to me that there were any new inventions in this paper. The authors definitely achieved their goals.