Don't Ever Leave Me, You Disgusting Monster: Computational Insights Into Moral Inference Updating in Borderline Personality Disorder.

Borderline personality disorder (BPD) is a serious mental illness characterized by volatility in mood, social attachments, and self-concept (1). A core feature of BPD is instability in interpersonal relationships. Individuals with BPD have intense and chaotic attachments to others, characterized by an often cyclical pattern of idealization followed by devaluation ( “ splitting ” ). The shift between these two extremes is often abrupt and seemingly out of proportion to the eliciting event. Splitting is often accompanied by intensely dysphoric emotional states, which in turn drive highly impulsive and typically maladaptive behaviors (e.g., self-injury, substance abuse, and reckless spending) (1). Interpersonal disturbances are responsible for much of the distress and impairment and are key targets for psychotherapeutic interventions. Yet despite the severe subjective distress, functional impairment, and economic burden imposed by interpersonal symptoms in BPD (2), their underlying cognitive and neurobiological mechanisms are only beginning to be identi fi ed. In the current issue of Biological Psychiatry: Cognitive Neuroscience and Neuroimaging , Siegel et al. (3) make a signi fi cant contribution to our understanding of these mechanisms by using an innovative computational cognition approach to understanding how patients with BPD generate and update moral inferences about other agents.