# A hierarchical examination of the neural and computational mechanisms underlying loneliness

> **NIH NIH DP5** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2024 · $405,940

## Abstract

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.

## Key facts

- **NIH application ID:** 10923384
- **Project number:** 1DP5OD037383-01
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Shawn A Rhoads
- **Activity code:** DP5 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $405,940
- **Award type:** 1
- **Project period:** 2024-09-19 → 2029-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10923384

## Citation

> US National Institutes of Health, RePORTER application 10923384, A hierarchical examination of the neural and computational mechanisms underlying loneliness (1DP5OD037383-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10923384. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*