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Linking Personality Traits to Individual Differences in Affective Spaces

机译:将个性特征与情感空间的个体差异联系起来

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Different individuals respond differently to emotional stimuli in their environment. Therefore, to understand how emotions are represented mentally will ultimately require investigations into individual-level information. Here we tasked participants with freely arranging emotionally-charged images on a computer screen according to their subjective emotional similarity (yielding a unique affective space for each participant) and subsequently sought external validity of the layout of the individuals’ affective spaces through the five-factor personality model (Neuroticism, Extraversion, Openness to Experience, Agreeableness, Conscientiousness) assessed via the NEO Five-Factor Inventory. Applying agglomerative hierarchical clustering to the group-level affective space revealed a set of underlying affective clusters whose within-cluster dissimilarity, per individual, was then correlated with individuals’ personality scores. These cluster-based analyses predominantly revealed that the dispersion of the negative cluster showed a positive relationship with Neuroticism and a negative relationship with Conscientiousness, a finding that would be predicted by prior work. Such results demonstrate the non-spurious structure of individualized emotion information revealed by data-driven analyses of a behavioral task (and validated by incorporating psychological measures of personality) and corroborate prior knowledge of the interaction between affect and personality. Future investigations can similarly combine hypothesis- and data-driven methods to extend such findings, potentially yielding new perspectives on underlying cognitive processes, disease susceptibility, or even diagnostic/prognostic markers for mental disorders involving emotion dysregulation.
机译:不同的个人对环境中的情绪刺激不同。因此,了解如何在精神上代表情绪,最终需要调查个人级信息。在这里,我们任务参与者根据其主观情感相似性自由在计算机屏幕上安排情绪上充电的图像(为每个参与者产生独特的情感空间)并随后通过五因素寻求个人情感空间布局的外部有效性通过Neo五因素库存评估了个性模型(神经质,倾向,经验,令人满意,令人满意的开放性,令人满意的开放性的良好开放性。将附聚层次聚类应用于组级的情感空间,揭示了一组底层的情感集群,每个人的内部不相似性,然后与个人的个性分数相关联。这些基于群集的分析主要揭示了负面聚类的分散与神经细胞的阳性关系和与消费性的负面关系,这一发现是通过前工作来预测的。这种结果证明了行为任务的数据驱动分析(并通过纳入人格的心理措施验证的数据驱动分析,并证实了对情感与人格之间的相互作用的先验知识的个性化的情感信息的非虚假结构。未来的调查可以类似地结合假设和数据驱动的方法来扩展这些发现,可能产生关于涉及情绪失调的精神障碍的潜在认知过程,疾病易感性甚至诊断/预后标志物的新观点。

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