# Optimized Affective Computing Measures of Social Processes and Negative Valence in Youth Psychopathology

> **NIH NIH R01** · CHILDREN'S HOSP OF PHILADELPHIA · 2021 · $861,615

## Abstract

ABSTRACT
Difficulties with emotion expression and social behavior characterize multiple psychiatric conditions and
negatively impact child development. However, existing measurement tools for indexing social-emotional
function are imprecise and subjective, or require specialized training that is costly and time-intensive, prohibiting
widespread implementation. The imprecision of existing tools has a major negative impact not only on research,
but on the ability to assess and treat individuals with mental health concerns – especially among underserved
and under-resourced populations. Here, we propose to address this problem by quantifying social and emotional
behavior using novel biobehavioral markers derived from computer vision (facial expression analysis) and
computational linguistics (social/sentiment analysis). Our team has successfully used these markers to predict
the presence of autism spectrum disorder (ASD) with 91% accuracy. In this proposal, we determine the extent
to which our markers can serve as continuous measures of social behavior and negative emotion to advance
clinical phenotyping and interventions. The proposal brings together two high-bandwidth clinical research
programs at the Children’s Hospital of Philadelphia and Baylor College of Medicine to collect data on 750
adolescents (ages 12-17 inclusive) with ASD, a primary anxiety or depressive disorder, or without any
developmental/psychiatric condition. At a single assessment, all youth will participate in an extensive clinical
phenotyping battery consisting of validated clinical interviews and child-/parent-report scales assessing
converging and diverging mental health constructs, and three tasks eliciting positive/negative emotion, social
stress, and mild frustration. A subsample of 150 adolescents will be reassessed 6-10 weeks later to allow
retest/stability analyses. A novel camera apparatus will capture naturalistic synchronized verbal and nonverbal
signals from dyads. Our analytic approach combines state-of-the-art machine learning, computational linguistics,
and computer vision – including facial emotion recognition methods that rival several commonly used
alternatives. The ultimate goal of this proposal is to develop valid and objective measures of the Social and
Negative Valence Systems using novel biobehavioral markers in a large transdiagnostic sample of youth.
Secondary goals are to develop easy-to-follow methods to widely disseminate our tools and procedures, and to
characterize individual variability in these key RDoC metrics by age, gender, race/ethnicity, and diagnosis. The
achievement of these goals will provide researchers with sorely needed measures of social and emotional
behavior, and provide clinicians with a new set of tools for identifying and tracking youth in need of mental health
treatment.

## Key facts

- **NIH application ID:** 10183399
- **Project number:** 1R01MH125958-01
- **Recipient organization:** CHILDREN'S HOSP OF PHILADELPHIA
- **Principal Investigator:** JOHN David HERRINGTON
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $861,615
- **Award type:** 1
- **Project period:** 2021-04-02 → 2026-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10183399

## Citation

> US National Institutes of Health, RePORTER application 10183399, Optimized Affective Computing Measures of Social Processes and Negative Valence in Youth Psychopathology (1R01MH125958-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10183399. Licensed CC0.

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