Artificial intelligence predicts personality by looking a person in the eyes

A popular saying by William Shakespeare, which has been repeated countless of times, is that of the eyes being the window to the soul, revealing what people think and how people feel. Now, new research reveals that eyes may also be an indicator of a person’s personality type, simply by the way they move.

A research that uses state-of-the-art machine learning algorithms to demonstrate a link between personality and eye movements was developed by the University of South Australia (UniSA) in partnership with the University of Stuttgart, Flinders University and the Max Planck Institute for Informatics in Germany.

According to the report made by UniSA, findings show that people’s eye movements reveal whether they are sociable, conscientious or curious, with the algorithm software reliably recognising four of the Big Five personality traits: neuroticism, extroversion, agreeableness, and conscientiousness.

In the course of the study, researchers tracked the eye movements of 42 participants as they went on doing their everyday tasks around a university campus.

Binocular-gaze data were tracked with the use of a state-of-the-art video-based eye tracker from SensorMotoric Instruments (SMI) at 60 Hz. The tracker was mounted on the head and recorded gaze data, along with a high-resolution scene video on a mobile phone that was carried in a cross-body bag.

The personality traits of the participants in the study were then cross-checked, subsequently, with the use of three well-established self-report questionnaires.

Dr Tobias Loetscher, from UniSA, explained that the study provides new links between previously under-investigated eye movements and personality traits.

The study, he added, delivered important insights for emerging fields of social signal processing and social robotics.

He shared that people are always looking for improved, personalised services. However, the robots and computers of today are not socially aware so they cannot adapt to non-verbal cues. But with this study, he countered, there is certainly the potential for these findings to improve human-machine interactions.

The research they are undertaking, he said, provides them the opportunity to develop robots and computers so that they can become more natural, and better at interpreting human social signals. This can actually revolutionise how humans communicate with machines.

Findings from the research have provided an important bridge between tightly controlled laboratory studies and the study of natural eye movements in real-world environments.

Dr Loetscher explained the difference by saying that their research has tracked and measured the visual behaviour of people going about their everyday tasks, which elicited more natural responses than if they were in a lab.

He gave credit to their machine-learning approach because not only did they validate the role of personality in explaining eye movement in everyday life, but they also revealed that new eye movement characteristics are predictors of personality traits.

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