Allgemeine und Neurokognitive Psychologie, Free University Berlin, Berlin, Germany.
PLoS One. 2011;6(8):e23743. doi: 10.1371/journal.pone.0023743. Epub 2011 Aug 24.
Our knowledge about affective processes, especially concerning effects on cognitive demands like word processing, is increasing steadily. Several studies consistently document valence and arousal effects, and although there is some debate on possible interactions and different notions of valence, broad agreement on a two dimensional model of affective space has been achieved. Alternative models like the discrete emotion theory have received little interest in word recognition research so far. Using backward elimination and multiple regression analyses, we show that five discrete emotions (i.e., happiness, disgust, fear, anger and sadness) explain as much variance as two published dimensional models assuming continuous or categorical valence, with the variables happiness, disgust and fear significantly contributing to this account. Moreover, these effects even persist in an experiment with discrete emotion conditions when the stimuli are controlled for emotional valence and arousal levels. We interpret this result as evidence for discrete emotion effects in visual word recognition that cannot be explained by the two dimensional affective space account.
我们对于情感过程的认识在不断加深，尤其是在情感对认知需求的影响方面，如文字处理。多项研究一致记录了情感效价和唤醒度的影响，尽管对于可能的相互作用和不同的情感效价概念存在一些争议，但对于情感空间的二维模型已经达成了广泛的共识。到目前为止，离散情绪理论等替代模型在字词识别研究中几乎没有受到关注。通过逐步向后淘汰和多元回归分析，我们发现，五种离散情绪（即高兴、厌恶、恐惧、愤怒和悲伤）能够解释与两个已发表的维度模型一样多的方差，假设效价是连续的或分类的，其中高兴、厌恶和恐惧这三个变量对这一结果有显著贡献。此外，当刺激的情绪效价和唤醒度水平得到控制时，即使在具有离散情绪条件的实验中，这些效应仍然存在。我们将这一结果解释为视觉字词识别中存在离散情绪效应的证据，这些效应不能用二维情感空间模型来解释。
