Data-driven analysis of gaze patterns in face perception: Methodological and clinical contributions

• Published in: Cortex Vol. 147; pp. 9 - 23
• Main Authors: Masulli, Paolo, Galazka, Martyna, Eberhard, David, Johnels, Jakob Åsberg, Gillberg, Christopher, Billstedt, Eva, Hadjikhani, Nouchine, Andersen, Tobias S.
• Format: Journal Article
• Language: English
• Published: Italy Elsevier Ltd 01-02-2022
• Subjects: Adult; Autism; Autistic Disorder; Depression; Eye; Eye tracking; Face; Face perception; Facial Recognition; Feature selection; Fixation, Ocular; Humans; Left visual field bias; Neurosciences; Neurovetenskaper; Principal component analysis; Left visual field bias; Autism; Face perception; Feature selection; Depression; Eye tracking; Principal component analysis
• ISSN: 0010-9452; 1973-8102
• DOI: 10.1016/j.cortex.2021.11.011

Gaze patterns during face perception have been shown to relate to psychiatric symptoms. Standard analysis of gaze behavior includes calculating fixations within arbitrarily predetermined areas of interest. In contrast to this approach, we present an objective, data-driven method for the analysis of gaze patterns and their relation to diagnostic test scores. This method was applied to data acquired in an adult sample (N = 111) of psychiatry outpatients while they freely looked at images of human faces. Dimensional symptom scores of autism, attention deficit, and depression were collected. A linear regression model based on Principal Component Analysis coefficients computed for each participant was used to model symptom scores. We found that specific components of gaze patterns predicted autistic traits as well as depression symptoms. Gaze patterns shifted away from the eyes with increasing autism traits, a well-known effect. Additionally, the model revealed a lateralization component, with a reduction of the left visual field bias increasing with both autistic traits and depression symptoms independently. Taken together, our model provides a data-driven alternative for gaze data analysis, which can be applied to dimensionally-, rather than categorically-defined clinical subgroups within a variety of contexts. Methodological and clinical contribution of this approach are discussed.