Description:

Emotion is a powerful state that can shape how we engage in interpersonal interactions, and an accurate evaluation of the affective state of the self and others is crucial to successful navigation of our social world. In order to better understand human emotions, I aim to dissect the precise dynamics of affective processes – specifically, the integration of top-down (i.e., contextual cues) and bottom-up (i.e., features of the human face that communicate an emotional response) information. To this end, my research leverages the emotional ambiguity found in the facial expressions of others, from which we glean many layers of social information that guide our subsequent behavior. In my talk, I will present a series of studies employing neuroimaging, social perception, and computational approaches that elucidate a corticolimbic neural circuitry that underpins the dynamic and integrative affective process in which emotionally ambiguous faces become contextualized. I will also offer a novel way to compute a trait-like individual difference measure that indexes one’s behavioral tendency to put more emphasis on contextual cues versus stimulus-driven features during emotional decision-making processes. These findings converge with my previous line of work that investigated the neural underpinnings of the idiosyncrasies in trait anxiety, both of which support the idea that an efficient crosstalk between the amygdala and the prefrontal cortex is important for the successful management of socioemotional information.