Carlisi Christina O, Reed Kyle, Helmink Fleur G L, Lachlan Robert, Cosker Darren P, Viding Essi, Mareschal Isabelle
Division of Psychology and Language Sciences, Developmental Risk and Resilience Unit, University College London, 26 Bedford Way, London WC1H 0AP, UK.
Department of Computer Science, University of Bath, 1 West, Claverton Down, Bath BA2 7AY, UK.
R Soc Open Sci. 2021 Oct 13;8(10):202251. doi: 10.1098/rsos.202251. eCollection 2021 Oct.
Emotional facial expressions critically impact social interactions and cognition. However, emotion research to date has generally relied on the assumption that people represent categorical emotions in the same way, using standardized stimulus sets and overlooking important individual differences. To resolve this problem, we developed and tested a task using to derive assumption-free, participant-generated emotional expressions. One hundred and five participants generated a subjective representation of happy, angry, fearful and sad faces. Population-level consistency was observed for happy faces, but fearful and sad faces showed a high degree of variability. High test-retest reliability was observed across all emotions. A separate group of 108 individuals accurately identified happy and angry faces from the first study, while fearful and sad faces were commonly misidentified. These findings are an important first step towards understanding individual differences in emotion representation, with the potential to reconceptualize the way we study atypical emotion processing in future research.
情绪性面部表情对社会互动和认知有着至关重要的影响。然而，迄今为止的情绪研究通常依赖于这样一种假设，即人们以相同的方式表征分类情绪，使用标准化的刺激集并忽略重要的个体差异。为了解决这个问题，我们开发并测试了一项任务，该任务使用参与者生成的情绪表达来得出无假设的结果。105名参与者生成了快乐、愤怒、恐惧和悲伤面孔的主观表征。快乐面孔在群体层面上具有一致性，但恐惧和悲伤面孔表现出高度的变异性。在所有情绪中都观察到了较高的重测信度。另一组108名个体准确识别了第一项研究中的快乐和愤怒面孔，而恐惧和悲伤面孔则经常被误认。这些发现是朝着理解情绪表征中的个体差异迈出的重要第一步，有可能在未来的研究中重新概念化我们研究非典型情绪加工的方式。
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