When we talk about operating “on autopilot,” we usually mean acting with little thought or effort, often resulting in error. It turns out actual autopilot systems can cause actual pilots to behave in this way.
“Early in my career, the concern in aviation was about stress and workload, but automation was becoming more of a concern,” says Alan Pope, who began his long stint at Langley Research Center in 1980. As automated flight control systems took over more and more of the moment-to-moment operations in the cockpit, pilots could grow bored, complacent, and disengaged—conditions that are just as dangerous as being overwhelmed, Pope explains.
By the mid-1990s, researchers at NASA and the Federal Aviation Administration, including Pope, were researching the causes of these “hazardous states of awareness” and ways to objectively identify and measure a pilot’s cognitive state. “We worked on finding physiological signals that showed underload,” he says.
Pope led a team looking for a way to use electroencephalographic (EEG) data—that is, brainwave readings—to quantify a subject’s level of engagement. The result, which they described in a 1995 paper, was an “engagement index” that’s still used today: the strength of high-frequency beta waves, which indicate attention, is divided by the combined power of lower-frequency alpha and theta waves, which come with relaxation, to arrive at a measure of engagement.
An incident later that year, in which a series of pilot errors ended with an American Airlines flight crashing into a mountain while preparing to land in Cali, Columbia, killing 159 people, intensified interest in the subject. The crash is mentioned more than once in a 2001 paper, in which Pope and two other researchers describe a collaboration between Langley and Old Dominion University to apply the engagement index to an attempt to train subjects to regulate their own engagement level.
The training relied on what’s known as a biofeedback loop. The researchers let the subjects in one group watch their own rating on the engagement index, on a 1-to-6 scale, while they performed a task, and told them to try to maintain a level of 3 or 4. Other groups were provided with incorrect feedback on engagement or none at all. After a week, all the participants were asked to perform the task again. The subjects in the group that had received the biofeedback training significantly outperformed the others while remaining closer to their baseline, “normal” engagement level. They also rated their workload markedly lower than those in the other groups did.
They had learned to regulate responses that are normally beyond our control. The paper concluded that this “physiological self-regulation” training could help pilots manage and maintain their attention.
When a group of Harvard University graduate students founded BrainCo Inc. in Cambridge, Massachusetts, in the spring of 2015, they didn’t know anything about NASA’s work on monitoring and mitigating hazardous states of awareness. But they did know a little about EEG readings as they related to attention and biofeedback.
The fledgling company wanted to develop a practical way to use EEG readings to monitor students’ attention in the classroom and, ultimately, to help people with attention deficit hyperactivity disorder (ADHD) gain more control over their concentration. A prototype EEG-reading, WiFi-connected headband had been built by the time Max Newlon was brought on board as a research scientist about a year later, but the company wanted to improve its software. Newlon was tasked with finding an algorithm to accurately determine cognitive state based on brainwave readings. He hit on the 2001 Langley/Old Dominion experiment and its use of Pope’s engagement index formula.
“I did a pretty extensive literature review and found that the NASA algorithm was the best fit for what we were doing,” he says.
BrainCo’s Focus EDU is a classroom system that lets the teacher monitor a class’s attention level in real time, as an average or as a “heat map” of the classroom, and it generates an after-class report on the group as a whole, as well as individual students’ attention levels.
An LED light on the front of each student’s headband can indicate one of three attention levels, although Newlon says the feature is generally turned off during class time. “You don’t want to distract students with their friends’ headband colors.”
Focus Family uses the same hardware with a smartphone app to create a sort of digital study buddy for use at home, Newlon says, noting that students can see their attention levels in real time and generate a report after a study session. A couple of video games are included, which users control by modulating their brainwaves. These assess abilities like sustained attention and task switching. All the results can be shared and compared in an online community.