Using genetic algorithms to uncover individual differences in how humans represent facial emotion.
Using genetic algorithms to uncover individual differences in how humans represent facial emotion.
复制标题
DOI：
10.1098/rsos.202251
复制
发表时间：
2021-10
影响因子：
3.5
通讯作者：
Mareschal I
中科院分区：
文献类型：
--
作者：
Carlisi CO;Reed K;Helmink FGL;Lachlan R;Cosker DP;Viding E;Mareschal I
情绪面部表情对社会互动和认知有着至关重要的影响。然而，到目前为止，情绪研究通常依赖于这样的假设，即人们以同样的方式表达绝对的情绪，使用标准化的刺激集，并忽略了重要的个体差异。为了解决这个问题，我们开发并测试了一项使用遗传算法的任务，以获得无假设的、参与者生成的情绪表达。105名参与者对高兴、愤怒、恐惧和悲伤的面孔进行了主观表征。在人群水平上，人们观察到了快乐面孔的一致性，但恐惧和悲伤的面孔表现出高度的变异性。所有情绪的重测信度都很高。在第一项研究中，由108人组成的另一组人准确地辨别出了高兴和愤怒的面孔，而恐惧和悲伤的面孔通常被误认了。这些发现是理解情绪表征的个体差异的重要第一步，有可能在未来的研究中重新构思我们研究非典型情绪加工的方式。
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 genetic algorithms 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.
登录
查看更多内容
影响因子：
9.2
作者：
Feinstein, Justin S;Adolphs, Ralph;Damasio, Antonio;Tranel, Daniel
通讯作者：
Tranel, Daniel
影响因子：
4.2
作者：
Barrett LF
通讯作者：
Barrett LF
影响因子：
3.5
作者：
Ersotelos, Nikolaos;Dong, Feng
通讯作者：
Dong, Feng
影响因子：
5.3
作者：
Fast, Cynthia D.;McGann, John P.
通讯作者：
McGann, John P.
影响因子：
6.2
作者：
Chai, JX;Hodgins, JK
通讯作者：
Hodgins, JK