# A Computational Approach to Defining Extinction and Habituation in Treatment of Affective Disorders

> **NIH NIH R21** · UNIVERSITY OF COLORADO DENVER · 2020 · $186,614

## Abstract

PROJECT SUMMARY/ABSTRACT
 This project will develop a novel computational model and test to measure the mechanism of exposure
therapy, which is used to treat threat-based psychopathology. Threat-based psychopathologies, such as
anxiety disorders, are the most common form of mental illness and the sixth-leading cause of disease-related
disability worldwide. Exposure therapy is the most widely used psychological treatment for threat-based
disorders. During exposure therapy, a person's invalid, pathologic threat cue-associations are identified, and
the person is trained against a pathologic threat response by exposure to the cue. Exposure therapy has
strong theoretical underpinnings in learning. However, its mechanisms have been conceptually formulated but
not formally defined and tested.
 This project takes the first step in addressing the gap between a theory of exposure therapy
mechanism and ability to measure it, by developing a formal expression of theoretical mechanisms in a
computational model. This formal specification and validation with human subjects may improve translation
between animal and human studies, provide targets for neurophysiological investigations, and contribute to
increasing the efficacy and efficiency of exposure-based treatments.
 To achieve this goal, we will extend CompAct (Competitive Activation), a computational model
describing the interactive dynamics of associative learning and attention, or attention learning. The principles of
attention learning have been well established in both humans and other species. CompAct has demonstrated
excellent predictive performance in tasks designed to probe attention learning. To measure individual
differences in threat processing, we will develop a novel, clinically relevant attention-learning task. By
measuring affective cue salience, affective cue associations, and their changes during learning, the model can
explain both extinction (reduction of acquired threat associations) and habituation (reduction of salience of
threat cues). Extinction and habituation are two primary mechanisms that have received empirical support in
exposure therapy. In addition to development, we aim to validate the novel task and CompAct. In two large
normative samples (N's=1000), we will parametrically characterize individual differences in the dynamics of
affective cue salience and associations. We will then associate these differences to individual levels of anxiety
symptoms, which are continuous with anxiety disorders, a threat-based psychopathology for which exposure
therapy is indicated.

## Key facts

- **NIH application ID:** 9996800
- **Project number:** 5R21MH120741-02
- **Recipient organization:** UNIVERSITY OF COLORADO DENVER
- **Principal Investigator:** Matt Jones
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $186,614
- **Award type:** 5
- **Project period:** 2019-08-15 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9996800

## Citation

> US National Institutes of Health, RePORTER application 9996800, A Computational Approach to Defining Extinction and Habituation in Treatment of Affective Disorders (5R21MH120741-02). Retrieved via AI Analytics 2026-06-08 from https://api.ai-analytics.org/grant/nih/9996800. Licensed CC0.

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