# Using Computational Modeling to Test Reinforcement Learning as a Predictor of Response in Family-Based Treatment for Adolescent Anorexia Nervosa

> **NIH NIH K23** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2023 · $196,344

## Abstract

PROJECT SUMMARY/ABSTRACT
Anorexia nervosa (AN) is associated with significant risk for deadly medical complications and an annual cost to
the US of around $11.2 billion. Although Family-Based Treatment (FBT) for adolescent AN has demonstrated
effectiveness in targeting symptoms of AN, up to 60% of individuals who receive FBT do not remit fully. Notably,
no prior work has explored neurocognitive predictors of FBT response, which may help to facilitate the
identification of treatment mechanisms and formulation of targeted treatments for non-responders. When
considering what neurocognitive processes may be implicated in FBT response, increasing work suggests that
adult AN may be characterized by alterations in reinforcement learning. Further, work in other forms of
psychopathology suggests that reinforcement learning may predict response to behavioral treatments. However,
few studies to date have tested alterations between reinforcement learning and treatment outcome, and none
have explored associations between reinforcement learning and FBT outcome. The current investigation will
leverage methods from cognitive neuroscience and computational modeling to explore reinforcement learning in
adolescents with AN (n = 58) and healthy control subjects (n = 58), as well as its associations with treatment
outcome in FBT. I will test the following hypotheses: Aim 1: Consistent with existing data in adults, the AN group
will demonstrate poorer performance in the learning task compared to HC, decreased loss learning, and poorer
exploitation of prior learned information. Aim 2: Within the AN group, lower rates of learning from loss, as well
as lower explore/exploit parameter values will relate to poorer outcomes at 1- and 6-month follow-ups,
operationalized as lower body weight and greater eating disorder cognitive symptoms. With the mentorship of
five experts across biostatistics, adolescent clinical research, computational modeling, and cognitive
neuroscience, the current patient-oriented career development award will allow me access to training that will
facilitate unique expertise at the intersection of these fields. Short-term, data from the current investigation will
yield insights that can be used to understand the persistence of AN symptoms and identify potential methods to
improve treatment outcomes. Long-term, the current project will allow me to launch my career and take the next
steps in a programmatic line of research merging complementary expertise in neurocognition, computational
methods, and adolescent intervention development.

## Key facts

- **NIH application ID:** 10693328
- **Project number:** 5K23MH131871-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Erin E. Reilly
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $196,344
- **Award type:** 5
- **Project period:** 2022-09-01 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10693328

## Citation

> US National Institutes of Health, RePORTER application 10693328, Using Computational Modeling to Test Reinforcement Learning as a Predictor of Response in Family-Based Treatment for Adolescent Anorexia Nervosa (5K23MH131871-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10693328. Licensed CC0.

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