Research

The Yu Emotion Science Lab (YES Lab) seeks to understand the relationship between emotion and morality. As the relationship is bidirectional, our research branches in two broad directions: how emotions are generated and how they are evaluated. To understand the generation process, we study neurocognitive mechanisms that transform features of social events into emotions. For the evaluation process, we investigate how perceivers integrate emotions that an agent displays (or fails to do so) into their judgment of the agent’s behaviors and their impressions of the agent’s moral character.

We show how antecedents of gratitude are integrated in the brain to give rise to gratitude (Yu et al., 2018).
We show how antecedents of gratitude are integrated in the brain to give rise to gratitude (Yu et al., 2018).

How are emotions generated?

Emotions, especially social and moral emotions, are cognitively more sophisticated than they are often thought to be. A prominent theoretical framework, the appraisal theory of emotion, posits that emotions are generated by a series of cognitive processes where features of the emotion-eliciting event and its bearing on one’s survival are extracted and integrated. We develop interactive tasks to quantitively manipulate these features (e.g., intentionality, agency, expectancy) to examine how they are represented and integrated neurally, and contribute to emotions people experience. For example, using sophisticated brain network analysis we have shown how the antecedents of gratitude are represented and integrated in the brain to give rise to gratitude.

Emotions play a critical role in moral evaluation in the virtue ethics tradition tracing back to Aristotle.
Emotions play a critical role in moral evaluation in the virtue ethics tradition tracing back to Aristotle.

How are emotions integrated in moral evaluation?

Experiencing emotions of the right kind, to the right extent, and in the right circumstances is an integral part of being virtuous, both in virtue ethics and in everyday moral discourse. How we morally judge an agent’s action depends on the emotion(s) accompanying the action (e.g., “Bob visits his sick partner at hospital without feeling sadness or compassion.”). Current projects combining virtue ethics of emotion and computational models of impression aim to understand the neurocognitive processes through which emotions (or their absence) are integrated in person-perception and moral inference. Emotions can also be integrated prospectively in the deliberation of our own future behaviors, thereby weighing in the choices we would make. This may have non-trivial real-life consequences to ourselves and people around us. For example, in Maupassant’s story The Necklace, Mathilde would have acknowledged that she lost the necklace and immediately learnt that it was only a fake one, had she not tried to avoid shame and embarrassment. But because she cares so much about her public appearance, she sells everything she owns to buy a much more expensive real diamond necklace for the owner, and has lived miserably in debt since then. In the YES Lab, we seek to understand how people make decisions, sometimes costly, to avoid certain emotions.

We aim to understand how emotion, language and culture co-evolve.
We aim to understand how emotion, language and culture co-evolve.

Emotion, language, and culture

How do we know whether an American and a German feel the same way when one claims to feel ‘guilty’ whereas the other claims to feel ‘schuldig’? Perhaps the subjective experience is obscure to observers, however we can track and analyze the events that lead to the agent’s using a given emotional term to express themselves. By applying machine learning algorithms to large-scale corpus data (e.g., online social network, digital libraries), we are developing classifiers in different languages to discriminate emotional words on the basis of the appraisal theory. Comparing the classifiers in different languages could shed light on the interesting dynamics among cultural values, emotions and languages.