Meet The Scientist: David Stanley, Ph.D.

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Neurable Team

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Dr. David Stanley thinks it’s time for neurotechnology to go mainstream.

“Scientists have been measuring brain activity since the early 20th century, but it’s been largely confined to clinical and experimental use,” he says. “I want to help transform this technology into something that improves everyday life.”

As Neurable’s Senior Machine Learning Engineer, Stanley is well positioned to usher in this transformation. His job involves, among other things, working on algorithms that translate brain activity into practical insights. This work has been integral to the development of Neurable’s headphones, which offer users real-time information about their state of focus–precisely the type of useful, everyday application that, Stanley hopes, will make brain tech relatable to a wider audience.

Today, Stanley’s passion for neurotechnology is palpable. During college, however, it wasn’t clear that he’d go into the field at all. Originally on track to become a physicist, it wasn’t until his senior year at the University of Toronto that a course on cellular bioelectricity inspired him to pursue graduate studies in computational neuroscience. He went on to complete a Ph.D. at the University of Florida and then a postdoc at Boston University, where he built mathematical models of complex brain systems.

After spending some time in academia, Stanley was drawn to Neurable for the opportunity to get hands-on experience at the cutting edge of neurotech. Now, his role is about as hands-on as it gets.

“At Neurable, I build models that help people gain insight into their cognitive state. For example, I’ve developed models that can predict how effectively someone is focusing on a task, or whether they are performing a certain facial gesture,” he says. “In the early stages of design, I will run short experiments on myself so I can tweak the model before scaling up to larger studies. This early and rapid iteration helps us spot any problems and improve the models accordingly.”

For all of his contributions to Neurable and the field, Stanley was recently awarded a 2021 Tech Innovator award by Built In. Granted to 50 leading technologists, the award honors “commitment to innovation and barrier-breaking.” Pleasantly surprised to receive such recognition, Stanley says that the award filled him with a sense of gratitude to be able to work in such a thrilling field.

“The best part of my job is getting to build exciting new technologies and the eureka moment of interacting with that technology in real time–and to feel that it truly is working,” he says.

“The best part of my job is getting to build exciting new technologies and the eureka moment of interacting with that technology in real time.”

Stanley looks forward to the day when more people can see neurotechnology in action–when the field truly goes mainstream. Still, he recognizes that serious work remains to be done before that day arrives.

“In addition to technological challenges, we have to contend with hurdles related to social acceptance and getting the neuroethics right,” he says. “ I’m excited to take on these challenges and I believe the benefits to society will be worth it.

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‍²Distraction Stroop Tasks experiment: The Stroop Effect (also known as cognitive interference) is a psychological phenomenon describing the difficulty people have naming a color when it's used to spell the name of a different color. During each trial of this experiment, we flashed the words “Red” or “Yellow” on a screen. Participants were asked to respond to the color of the words and ignore their meaning by pressing four keys on the keyboard –– “D”, “F”, “J”, and “K,” -- which were mapped to “Red,” “Green,” “Blue,” and “Yellow” colors, respectively. Trials in the Stroop task were categorized into congruent, when the text content matched the text color (e.g. Red), and incongruent, when the text content did not match the text color (e.g., Red). The incongruent case was counter-intuitive and more difficult. We expected to see lower accuracy, higher response times, and a drop in Alpha band power in incongruent trials. To mimic the chaotic distraction environment of in-person office life, we added an additional layer of complexity by floating the words on different visual backgrounds (a calm river, a roller coaster, a calm beach, and a busy marketplace). Both the behavioral and neural data we collected showed consistently different results in incongruent tasks, such as longer reaction times and lower Alpha waves, particularly when the words appeared on top of the marketplace background, the most distracting scene.

‍Interruption by Notification: It’s widely known that push notifications decrease focus level. In our three Interruption by Notification experiments, participants performed the Stroop Tasks, above, with and without push notifications, which consisted of a sound played at random time followed by a prompt to complete an activity. Our behavioral analysis and focus metrics showed that, on average, participants presented slower reaction times and were less accurate during blocks of time with distractions compared to those without them.

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