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

Paolo Masulli1, Martyna Galazka2, David Eberhard2

  • 1Department of Applied Mathematics and Computer Science DTU Compute, Section of Cognitive Systems, Technical University of Denmark, Kgs. Lyngby, Denmark; iMotions A/S, Copenhagen V, Denmark.

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Summary

This study introduces a data-driven method to analyze gaze patterns in psychiatric patients, revealing links between eye movements and autism traits and depression symptoms. The approach offers a new way to understand clinical subgroups through objective gaze analysis.

Keywords:
AutismDepressionEye trackingFace perceptionFeature selectionLeft visual field biasPrincipal component analysis

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Computational Psychology

Background:

  • Gaze patterns during face perception are linked to psychiatric conditions.
  • Traditional analysis uses fixed areas of interest, which may be limiting.
  • An objective, data-driven approach is needed for analyzing gaze behavior in clinical populations.

Purpose of the Study:

  • To develop and validate an objective, data-driven method for analyzing gaze patterns in relation to psychiatric symptoms.
  • To investigate the relationship between dimensional symptom scores (autism, attention deficit, depression) and gaze behavior.
  • To provide an alternative to traditional, arbitrary methods of gaze analysis.

Main Methods:

  • Applied a novel, data-driven method to analyze gaze patterns in 111 psychiatric outpatients viewing faces.
  • Utilized Principal Component Analysis (PCA) coefficients to model gaze data.
  • Employed linear regression to correlate gaze pattern components with dimensional symptom scores for autism, attention deficit, and depression.

Main Results:

  • Specific gaze pattern components significantly predicted autistic traits and depression symptoms.
  • Confirmed that gaze shifts away from eyes with increasing autistic traits.
  • Identified a lateralization effect: reduced left visual field bias correlated with increased autistic traits and depression symptoms.

Conclusions:

  • The proposed data-driven method offers an objective alternative for analyzing gaze patterns in psychiatric research.
  • This approach can be applied to dimensionally-defined clinical subgroups, enhancing diagnostic and research capabilities.
  • Findings highlight the utility of objective gaze analysis for understanding the neurobiological underpinnings of psychiatric conditions.