Project Summary In May 2023, the Surgeon General declared a public health epidemic of social isolation and loneliness in the United States. Loneliness—the subjective experience of distress at one's perceived lack of social connection—has reached an all-time high in the United States, with roughly half of U.S. adults reporting sometimes or always feeling lonely. It is a key transdiagnostic symptom across psychopathology and serious risk for many deleterious outcomes, including depression, anxiety, dementia, stroke, inflammation, cardiovascular disease, cancer, suicide, and premature death. The premature mortality rate associated with social isolation and loneliness is greater than that caused by smoking 15 cigarettes per day and nearly three times that of obesity. With the enormous economic burden—more than $6.7 billion in healthcare spending annually among older adults alone—addressing this problem is a public health emergency. Despite this, the neural, cognitive, and behavioral mechanisms that underlie forming and maintaining interpersonal relationships at the individual-level are still poorly characterized—which is crucial for generating new policies, health care, and preventative interventions. This proposal aims to characterize how and when computations along a cognitive hierarchy of social information processing (e.g., perception, learning, inference, decision-making) impact social connection, and consequently, loneliness. Because loneliness can emerge via disrupted computations at multiple points along this hierarchy, the proposed project will utilize state-of-the-art computational, behavioral, and neural methods and apply theory-based as well as data-driven models of brain and behavior to identify computational phenotypes of loneliness. Aim 1 is to assess how loneliness impacts the motivation to seek social connections via the perception of social cues. Aim 2 is to examine how loneliness affects learning from social information, a key ability in forming social connections via. Aim 3 is to characterize how loneliness impacts the ability to infer others' beliefs and act accordingly, which is key for maintaining social connections. This work expands a new direction of interpersonal computational psychiatry: integrating theory with computational methods to understand the interplay between mental health outcomes and computations underlying interpersonal relationships, which is critical for identifying risk factors of chronic loneliness and designing more personalized interventions to prevent future epidemics.