New Issue: Orbital Catastrophe Ahead? Read Now

Eye-Tracking Software That Detects Mental States

The technology could help keep drowsy drivers awake

Join Our Community of Science Lovers!


On supporting science journalism

If you're enjoying this article, consider supporting our award-winning journalism by subscribing. By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today.


A car detects when a driver starts to nod off and gently pulls over. A tablet or laptop senses its user is confused and offers assistance. Such interventions seem futuristic, but in fact they may not require any technological breakthroughs: a recent study suggests that with the aid of a standard camera, a simple computer program can learn to read people's eye movements to determine what they are doing and perhaps how they are feeling.

Psychologists at the University of South Carolina were curious if a computer could figure out what a person was up to based on their eye movements. They first had 12 people engage in four tasks, including reading lines of text and searching photographs for a specific printed letter. Each person repeated the tasks 35 to 50 times while a camera recorded how their eyes moved. Using a subset of those data, the team trained a simple computer program, called a naive Bayes classifier, to identify which of the four tasks each person was doing. In the remaining trials, the classifier correctly determined which task the person was working on 75 percent of the time, well above the 25 percent expected by chance.

Because the computer program is based on a flexible algorithm that is simple but powerful, this set-up could most likely be used to identify emotions or mental states such as confusion or fatigue, the researchers suggest in the paper, which appeared in September 2013 in PLOS ONE. With only a brief training period, a car's onboard computer—existing models are more than powerful enough—could learn how a driver's gaze changed as he or she became more exhausted. Further work, the authors suggest, could lead to devices capable of identifying and aiding people in need of assistance in a variety of situations.

Subscribe to Support Independent Journalism

Great science journalism requires human expertise, time, effort and creativity. And it costs money. That’s why I and the journalists here at Scientific American hope you’ll join our community.

When you subscribe, you are supporting staff and freelance journalists who are passionate about telling science stories that are true, important and compelling. Our editors and reporters are often experts in their fields, which means they understand the nuances of big discoveries and can untangle the breakthroughs from the hype. With a subscription, you are also supporting rigorous fact-checking to ensure the words we publish are precise and accurate. And you’re supporting original illustrations, graphics and photos that bring you closer to an advanced laboratory, an ice sheet in Antarctica or a space mission in orbit. You’re helping us craft other types of high-quality journalism as well: Our newsletters are carefully written, edited and curated by staffers you have or will come to know and love. Our Science Quickly podcast is based on original reporting, collaboration with editors and scientists and exacting production.

Subscriptions keep this engine running so we can continue to deliver thoughtful, rigorous and independent science journalism to you. In an era of viral misinformation, this work is crucial. If you value what we do, I hope you’ll consider joining us as a subscriber

Thank you,

Jeanna Bryner, Editor in Chief, Scientific American

Subscribe