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A Pilot Study on the Decoding of Dynamic Emotional Expressions in Major Depressive Disorder

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Advances in Neural Networks (WIRN 2015)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 54))

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Abstract

Studies investigating on the ability of depressed patients to decode emotional expressions have mostly exploited static stimuli (i.e., static facial expressions of basic emotions) showing that (even though this was not always the case) depressed patients are less accurate (in literature this is reported as a bias) in decoding negative emotions (fear, sadness and anger). However, static stimuli may not reflect the everyday situations and therefore this pilot study proposes to exploit dynamic stimuli involving both visual and auditory channels. We recruited 16 outpatients with Recurrent Major Depressive Disorder (MDD) matched with 16 healthy controls (HC). Their competence to decode emotional expressions was assessed through an emotion recognition task that included short audio (without video), video (without audio) and audio/video tracks. The results show that depressed patients are less accurate than controls, even though with no statistical significant difference, in decoding fear and anger, but not sadness, happiness and surprise where differences are significant. This is independent of the communication mode (either visual, auditory, or both, even though MDDs perform more worse than HCs in audio/video) and the severity of depressive symptoms, suggesting that the MDDs poorer decoding accuracy towards negative emotions is latent and emerges only during and after stressful events. The poorer decoding accuracy of happiness and (positive) surprise can be due to anhedonia.

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Acknowledgments

The patients, the healthy controls and the doctors (psychiatrists) of the Mental Health Service Center in Avellino (Italy) are acknowledged for their participation and support to this study. This paper was inspired by the stimulating discussions raised during the participation of the first author in Dagstuhl Seminar 13451 “Computational Audio Analysis” held from Nov 3rd to 8th 2013 in Wadern, Germany.

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Authors and Affiliations

  1. Department of Psychology, Second University of Naples and IIASS, Caserta, Italy

    Anna Esposito

  2. Università di Napoli Federico II, Naples, Italy

    Filomena Scibelli

  3. Seconda Università di Napoli, Caserta, Italy

    Filomena Scibelli

  4. School of Computing Science, University of Glasgow, Glasgow, UK

    Alessandro Vinciarelli

Authors
  1. Anna Esposito
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  2. Filomena Scibelli
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  3. Alessandro Vinciarelli
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Corresponding author

Correspondence to Anna Esposito .

Editor information

Editors and Affiliations

  1. Computer Science Department, University of Milano, Milano, Italy

    Simone Bassis

  2. Department of Psychology, Seconda Università di Napoli and IIASS, Caserta, Italy

    Anna Esposito

  3. Dept. of Info., Mathematics, Ele & Trans, Univ. Mediterranea of Reggio Calabria, Reggio Calabria, Italy

    Francesco Carlo Morabito

  4. Dip. Elettronica e Telecomunicazioni, Politecnico di Torino, Torino, Italy

    Eros Pasero

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Esposito, A., Scibelli, F., Vinciarelli, A. (2016). A Pilot Study on the Decoding of Dynamic Emotional Expressions in Major Depressive Disorder. In: Bassis, S., Esposito, A., Morabito, F., Pasero, E. (eds) Advances in Neural Networks. WIRN 2015. Smart Innovation, Systems and Technologies, vol 54. Springer, Cham. https://doi.org/10.1007/978-3-319-33747-0_19

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  • DOI: https://doi.org/10.1007/978-3-319-33747-0_19

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