I am interested broadly in what it means for people to understand, especially in the sense that we find most valuable (as in the ‘aha’ moment of an insight when we suddenly understand something better) and the implications of this across a variety of domains. In studying these ideas, my work uses an interdisciplinary approach, which integrates social, cognitive, philosophical, and computational perspectives, and combines human behavioral studies and formal modelling

Within this, my research focuses on three areas:

1) Emotions: How can a computational approach to emotions shed light on the nature and function emotions, and the importance of understanding in resolving emotions?

· Example questions in this area include: What does viewing emotions as value-updating processes (e.g., within a reinforcement learning framework) tell us about the nature of emotions? From a computational-cognitive perspective, what does it mean to ‘resolve’ an emotion, why do people often struggle with this, and what role does ‘understanding’ one’s emotions play in helping to do this better?

2) Explanation: What makes for a subjectively good explanation (i.e., a subjectively good way of understanding something)?

· Example questions in this area include: why do we often prefer simpler explanations, and what computational and cognitive functions might this serve?

3) Stereotyping: How does our understanding go wrong, especially when we try to understand other people based on their groups (as in cases of stereotyping)?

· Example questions in this area include: How do ordinary people distinguish between problematic stereotype use and acceptable group-based generalizations? How can formal modelling (e.g., Bayesian models) help us identify biases in people’s impressions of others?

If you are interested in joining my lab, either as an undergraduate research assistant or as a graduate student, please check out my website for more information (especially the “Join the lab” page): https://thaliavrantsidis.com/