A pilot study on the decoding of dynamic emotional expressions in major depressive disorder

Esposito, A., Scibelli, F. and Vinciarelli, A. (2016) A pilot study on the decoding of dynamic emotional expressions in major depressive disorder. In: International Workshop on Neural Networks (WIRN 2015), Vietri sul Mare, Italy, 20-22 May 2015, pp. 189-200. ISBN 9783319337463 (doi: 10.1007/978-3-319-33747-0_19)

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.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Vinciarelli, Professor Alessandro
• Authors: Esposito, A., Scibelli, F., and Vinciarelli, A.
• College/School: College of Science and Engineering > School of Computing Science
• ISSN: 2190-3018
• ISBN: 9783319337463