# Neurocomputational substrates of maladaptive uncertainty learning and avoidance in anxiety

> **NIH NIH K23** · EMORY UNIVERSITY · 2024 · $170,777

## Abstract

This K23 application will provide the applicant, a clinical psychologist with expertise in neuroimaging and
computational modeling, with training and mentored research experience towards an independent research
career studying disrupted learning processes in anxiety disorders. Training activities will focus on: 1) clinically
informative applications of computational modeling and neuroimaging in anxiety, 2) advanced computational
modeling of uncertainty and exploration, and 3) ecological momentary assessment of behavioral avoidance.
This training will be facilitated by an interdisciplinary team of experts in computational and neural approaches
to understanding psychiatric disorders, neurally-informed computational modeling of uncertainty and
avoidance, and ecological assessment of clinically-relevant behaviors. Training will take place at the
Department of Psychiatry at the University of Pittsburgh, which has a long and successful track record of
supporting junior scientists. To fulfill these training goals, the proposed research adapts approaches from basic
neurocomputational studies on uncertainty and exploration to apply to anxiety. Specifically, the proposed
research will test the hypotheses that anxiety, particularly anxious arousal, is related to disrupted learning
about uncertain, aversive outcomes, as measured by neural and behavioral measures; that disrupted
uncertainty learning leads to avoidance of uncertain options in anxiety; and that measures of uncertainty
avoidance relate to real-world behavioral avoidance. Participants (n=85), oversampled for high anxiety, will
complete a task assessing uncertainty learning while undergoing fMRI scanning. They will then report on real-
world avoidance behaviors for two weeks. Participants’ performance on the uncertainty learning task will be fit
to a computational model to measure learning from uncertainty as well as the tendency to explore versus avoid
options based on uncertainty. Measures of uncertainty estimated from the computational model will be
regressed against fMRI BOLD signals and behavioral choices; these effects on neural and behavioral function
will be tested for differences with anxious arousal. Individual variation in uncertainty-dependent exploration will
be tested for concordance with participants’ current real-world reports of behavioral avoidance and if they
predict future real-world behavioral avoidance. The anticipated impact, in line with NIMH’s Strategic Objectives,
will be identification of a) neural mechanisms for a complex behavior, maladaptive behavioral avoidance, b)
objective assessments of anxiety and avoidance, and c) possible novel treatment targets.

## Key facts

- **NIH application ID:** 11040062
- **Project number:** 7K23MH122626-05
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** Vanessa Brown
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $170,777
- **Award type:** 7
- **Project period:** 2024-02-19 → 2025-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11040062

## Citation

> US National Institutes of Health, RePORTER application 11040062, Neurocomputational substrates of maladaptive uncertainty learning and avoidance in anxiety (7K23MH122626-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/11040062. Licensed CC0.

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