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Scibelli, Filomena; Troncone, Alda; Likforman-Sulem, Laurence; Vinciarelli, Alessandro; Esposito, Anna (2016)
Publisher: Frontiers Media S.A.
Journal: Frontiers in ICT
Languages: English
Types: Article
Subjects: dynamic stimuli, recurrent major depressive disorder, adjustment disorder with depressed mood, Electronic computers. Computer science, emotion recognition task, emotional decoding bias, ICT, basic emotions, QA75.5-76.95, communication channels

Classified by OpenAIRE into

mesheuropmc: behavioral disciplines and activities, mental disorders
Most studies investigating the processing of emotions in depressed patients reported impairments in the decoding of negative emotions. However, these studies adopted static stimuli (mostly stereotypical facial expressions corresponding to basic emotions) which do not reflect the way people experience emotions in everyday life. For this reason, this work proposes to investigate the decoding of emotional expressions in patients affected by recurrent major depressive disorder (RMDD) using dynamic audio/video stimuli. RMDDs’ performance is compared with the performance of patients with adjustment disorder with depressed mood (ADs) and healthy (HCs) subjects. The experiments involve 27 RMDDs (16 with acute depression – RMDD-A and 11 in a compensation phase – RMDD-C), 16 Ads, and 16 HCs. The ability to decode emotional expressions is assessed through an emotion recognition task based on short audio (without video), video (without audio), and audio/video clips. The results show that AD patients are significantly less accurate than HCs in decoding fear, anger, happiness, surprise, and sadness. RMDD-As with acute depression are significantly less accurate than HCs in decoding happiness, sadness, and surprise. Finally, no significant differences were found between HCs and RMDD-Cs in a compensation phase. The different communication channels and the types of emotion play a significant role in limiting the decoding accuracy.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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