Department of Psychology, The Ohio State University, 1835 Neil Avenue, Columbus, OH 43210, USA.
Emotion. 2012 Apr;12(2):268-82. doi: 10.1037/a0024218. Epub 2011 Jul 25.
The words used to describe emotions can provide insight into the basic processes that contribute to emotional experience. We propose that emotions arise partly from interacting evaluations of one's current affective state, previous affective state, predictions for how these may change in the future, and the experienced outcomes following these predictions. These states can be represented and inferred from neural systems that encode shifts in outcomes and make predictions. In two studies, we demonstrate that emotion labels are reliably differentiated from one another using only simple cues about these affective trajectories through time. For example, when a worse-than-expected outcome follows the prediction that something good will happen, that situation is labeled as causing anger, whereas when a worse-than-expected outcome follows the prediction that something bad will happen, that situation is labeled as causing sadness. Emotion categories are more differentiated when participants are required to think categorically than when participants have the option to consider multiple emotions and degrees of emotions. This work indicates that information about affective movement through time and changes in affective trajectory may be a fundamental aspect of emotion categories. Future studies of emotion must account for the dynamic way that we absorb and process information.
用来描述情绪的词语可以深入了解促成情绪体验的基本过程。我们提出，情绪部分源于对当前情绪状态、之前情绪状态、对未来这些状态可能如何变化的预测以及这些预测之后的实际结果的相互评估。这些状态可以通过编码结果变化并做出预测的神经系统来表示和推断。在两项研究中，我们证明仅通过有关这些随时间变化的情感轨迹的简单线索，就可以可靠地区分情绪标签。例如，当预测有好事发生之后出现了不如预期的结果时，这种情况被标记为引起愤怒，而当预测有坏事发生之后出现了不如预期的结果时，这种情况被标记为引起悲伤。当要求参与者进行分类思考时，情绪类别会得到更细致的区分，而当参与者可以考虑多种情绪和情绪程度时，则不会如此。这项工作表明，有关情感随时间推移的运动以及情感轨迹变化的信息可能是情绪类别的一个基本方面。未来的情绪研究必须考虑到我们吸收和处理信息的动态方式。
