They way you move your eyes may be a strong indicator of your personality type, according to a new study at the University of South Australia (UniSA)

UniSA researchers in partnership with the University of Stuttgart (Germany), Flinders University (Australia) and the Max Planck Institute for Informatics (Germany) used state-of-the-art machine-learning algorithms to demonstrate a link between personality and eye movements.

Previous studies that have shown an association between personality traits and eye movements suggest that people with similar traits tend to move their eyes in similar ways. Optimists, for example, are less likely to spend time inspecting negative emotional stimuli (e.g., skin cancer images) than pessimists.

In the new study, the researchers found that people’s eye movements can reveal whether they are sociable, conscientious or curious, with the algorithm software reliably recognizing four of the Big Five personality traits: neuroticism (the tendency to be nervous, insecure, self-critical or awkward), extraversion (gains energy from social interactions), agreeableness (the tendency to be tactful, sensitive, helpful and considerate) and conscientiousness ( the tendency to control impulses and act in socially acceptable ways).

For the study, the research team tracked the eye movements of 42 participants as they ran an errand around a university campus, and subsequently evaluated their personality traits using well-established questionnaires.

The researchers say the study reveals new links between previously under-investigated eye movements and personality traits and offers important insights for the emerging field of social signal processing as well as social robotics.

“There’s certainly the potential for these findings to improve human-machine interactions,” said Dr. Tobias Loetscher from UniSA. “People are always looking for improved, personalized services. However, today’s robots and computers are not socially aware, so they cannot adapt to non-verbal cues.

“This research provides opportunities to develop robots and computers so that they can become more natural, and better at interpreting human social signals.”

Loetscher added that the the findings also provide an important bridge between tightly controlled laboratory studies and the study of natural eye movements in real-world scenarios.

“This research has tracked and measured the visual behavior of people going about their everyday tasks, providing more natural responses than if they were in a lab.

“And thanks to our machine-learning approach, we not only validate the role of personality in explaining eye movement in everyday life, but also reveal new eye movement characteristics as predictors of personality traits.”

Source: University of South Australia