Unsupervised learning of facial emotion decoding skills

Frontiers in Human Neuroscience

Benjamin eSack¹  Irina eKomlewa¹  Katja eBroer¹  Silke eAnders¹  Jan Oliver Huelle¹ 

[1] Universität zu Lübeck;

关键词: Empathy;    Social learning;    Perceptual Learning;    cultural learning;    unsupervised learning;    emotional facial expressions;   

DOI  :  10.3389/fnhum.2014.00077

来源: DOAJ

【 摘 要 】

Research on the mechanisms underlying human facial emotion recognition has long focussed on genetically determined neural algorithms and often neglected the question of how these algorithms might be tuned by social learning. Here we show that facial emotion decoding skills can be significantly and sustainably improved by practise without an external teaching signal. Participants saw video clips of dynamic facial expressions of five different women and were asked to decide which of four possible emotions (anger, disgust, fear and sadness) was shown in each clip. Although no external information about the correctness of the participant’s response or the sender’s true affective state was provided, participants showed a significant increase of facial emotion recognition accuracy both within and across two training sessions two days to several weeks apart. We discuss several similarities and differences between the unsupervised improvement of facial decoding skills observed in the current study, unsupervised perceptual learning of simple stimuli described in previous studies and practise effects often observed in cognitive tasks.

【 授权许可】

Unknown