Genetic algorithms reveal profound individual differences in emotion recognition.
Genetic algorithms reveal profound individual differences in emotion recognition.
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DOI：
10.1073/pnas.2201380119
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发表时间：
2022-11-08
影响因子：
11.1
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中科院分区：
文献类型：
--
作者：
我们开发了一种遗传算法工具，允许用户改进面部表情的描述，直到他们达到他们认为反映特定情绪的表情应该是什么样子。该工具提供了一个有效的表情空间采样，非常适合于捕捉情绪识别中的个体差异。我们发现，通过我们的程序产生的表情的个体差异解释了情绪识别表现的差异。我们的发现通过证明相同的刺激可以在人们中引起不同的反应，从而推进了情绪处理的研究，这可能反映了个体在将其视为视觉类别实例的程度上的差异，而不是专门处理情感刺激的大脑机制的差异。情感交流依赖于表达者和观看者之间对表达特定情感的面部表情的相互理解。然而，我们不知道人们是否对情绪状态如何映射到面部表情有共同的理解。这是因为表达式存在于一个高维空间中，太大了，无法用传统的实验范式来探索。在这里，我们通过采用遗传算法并将其与逼真的三维化身相结合来有效地探索高维表达空间来解决这个问题。共有336人使用这些工具生成了代表快乐、恐惧、悲伤和愤怒的面部表情。我们发现通过我们的程序产生的表情有很大的差异，这表明不同的人将不同的面部表情与相同的情绪状态联系在一起。然后，我们通过要求人们对不同的测试表情进行分类，来检查所创造的面部表情的可变性是否可以解释在标准情绪识别任务中的表现差异。我们发现，情绪分类表现可以用测试表情与每个个体产生的表情相匹配的程度来解释。我们的研究结果揭示了人们对面部情绪表现的多样性，甚至在典型的成年人群体中也是如此。这对解释对情绪刺激的反应具有深远的意义，这可能反映了人们归因于特定面部表情的情绪类别的个体差异，而不是产生情绪反应的大脑机制的差异。
We developed a genetic algorithm tool allowing users to refine depictions of facial expressions until they reach what they think the expression reflecting a particular emotion should look like. The tool provides an efficient sampling of expression space, ideally suited for capturing individual differences in emotion recognition. We found that individual differences in the expressions subjects generated via our procedure account for differences in emotion recognition performance. Our discoveries advance research on emotion processing by demonstrating that the same stimulus can elicit different responses in people, which may reflect individual differences in the extent to which it is recognized as an instance of a visual category, rather than differences in brain mechanisms specialized to process affective stimuli. Emotional communication relies on a mutual understanding, between expresser and viewer, of facial configurations that broadcast specific emotions. However, we do not know whether people share a common understanding of how emotional states map onto facial expressions. This is because expressions exist in a high-dimensional space too large to explore in conventional experimental paradigms. Here, we address this by adapting genetic algorithms and combining them with photorealistic three-dimensional avatars to efficiently explore the high-dimensional expression space. A total of 336 people used these tools to generate facial expressions that represent happiness, fear, sadness, and anger. We found substantial variability in the expressions generated via our procedure, suggesting that different people associate different facial expressions to the same emotional state. We then examined whether variability in the facial expressions created could account for differences in performance on standard emotion recognition tasks by asking people to categorize different test expressions. We found that emotion categorization performance was explained by the extent to which test expressions matched the expressions generated by each individual. Our findings reveal the breadth of variability in people’s representations of facial emotions, even among typical adult populations. This has profound implications for the interpretation of responses to emotional stimuli, which may reflect individual differences in the emotional category people attribute to a particular facial expression, rather than differences in the brain mechanisms that produce emotional responses.
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