RESUMO
A major challenge in understanding the neurobiological basis of psychiatric disorders is rigorously quantifying subjective metrics that lie at the core of mental illness, such as low self-esteem. Self-esteem can be conceptualized as a 'gauge of social approval' that increases in response to approval and decreases in response to disapproval. Computational studies have shown that learning signals that represent the difference between received and expected social approval drive changes in self-esteem. However, it is unclear whether self-esteem based on social approval should be understood as a value updated through associative learning, or as a belief about approval, updated by new evidence depending on how strongly it is held. Our results show that belief-based models explain self-esteem dynamics in response to social evaluation better than associative learning models. Importantly, they suggest that in the short term, self-esteem signals the direction and rate of change of one's beliefs about approval within a group, rather than one's social position.
