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Pain and Fear in the Eyes: Gaze Dynamics Predicts Social Anxiety from Fear Generalisation

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Image Analysis and Processing - ICIAP 2023 Workshops (ICIAP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14365))

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Abstract

This study presents a systematic approach for analyzing eye movements in the context of fear generalisation and predicting Social Interaction Anxiety Scale (SIAS) scores. Leveraging principles from foraging theory, we introduce a composite Ornstein-Uhlenbeck (O-U) process as a computational model for social anxiety assessment based on eye-tracking data. Through Bayesian analysis, we infer the model parameters and identify a feature set for SIAS score prediction. The results demonstrate the effectiveness of our approach, achieving promising performance using Random Forest (RF) classification. This research offers a novel perspective on gaze analysis for social anxiety assessment and highlights the potential of gaze behaviour as a valuable modality for psychological evaluation.

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Author information

Authors and Affiliations

  1. PHuSe Lab - Università degli Studi di Milano, Milan, Italy

    Sabrina Patania, Alessandro D’Amelio, Matteo Limoncini, Marco Ghezzi, Vincenzo Conversano & Giuseppe Boccignone

  2. AImageLab - Università degli Studi di Modena e Reggio Emilia, Modena, Italy

    Vittorio Cuculo

Authors
  1. Sabrina Patania
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  2. Alessandro D’Amelio
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  3. Vittorio Cuculo
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  4. Matteo Limoncini
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  5. Marco Ghezzi
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  6. Vincenzo Conversano
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  7. Giuseppe Boccignone
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Corresponding author

Correspondence to Sabrina Patania .

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Editors and Affiliations

  1. University of Udine, Udine, Italy

    Gian Luca Foresti

  2. University of Udine, Udine, Italy

    Andrea Fusiello

  3. University of York, York, UK

    Edwin Hancock

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Patania, S. et al. (2024). Pain and Fear in the Eyes: Gaze Dynamics Predicts Social Anxiety from Fear Generalisation. In: Foresti, G.L., Fusiello, A., Hancock, E. (eds) Image Analysis and Processing - ICIAP 2023 Workshops. ICIAP 2023. Lecture Notes in Computer Science, vol 14365. Springer, Cham. https://doi.org/10.1007/978-3-031-51023-6_12

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  • DOI: https://doi.org/10.1007/978-3-031-51023-6_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-51022-9

  • Online ISBN: 978-3-031-51023-6

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