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.
