# Differentiating reward seeking and loss avoidance with reference-dependent learning models

> **NIH NIH R01** · COLUMBIA UNIV NEW YORK MORNINGSIDE · 2022 · $526,636

## Abstract

Project Summary
The differentiation between positive and negative valence is central to psychiatry. A seemingly categorical
distinction between the drive toward rewards vs. the effort to avoid punishment appears central to many
symptoms of psychiatric dysfunction and is evident in both how diagnostic categories are delineated and in
the definition of cross-diagnostic constructs in RDoC. However, while there has been major progress in
understanding how reward drives learning and actions and the underlying neural mechanisms, there has been
much less progress in understanding the mechanisms by which loss and punishment affect behavior. Indeed,
there has been continued controversy about whether the neural mechanisms of reward and loss are
dissociable at all. Studies of the neural bases of reward seeking vs. loss avoidance have yielded mixed
results, manifested both in inconsistent findings about shared vs. separate neural circuitry, and in surprising
results in psychiatric populations, for instance showing reward processing abnormalities in psychiatric
conditions that appear at face value to be driven by avoidance (e.g. OCD and anxiety). This has made it
virtually impossible to address the critical question of defining valid measurements for reward seeking vs. loss
avoidance separately, let alone for understanding the balance between them and their relation to other
dimensional constructs and psychopathology. Here we address this challenge. We build on a computational
framework that resolves the inconsistency in existing results by formalizing how avoiding a loss can – in
certain circumstances and in some people – be reframed as a reward. Here we advance the hypothesis that
using computational methods for quantifying and isolating this subjective reframing will allow us better to
disentangle the relative, covert contributions of reward seeking vs. loss avoidance, and clarify their neural
underpinnings. We propose to test this hypothesis by rigorously assessing the validity of the resulting
measures (compared to simpler measures of overt reward and loss behavior) across tasks, measures, and
test-retest replications. In particular, we address two specific aims. First, we seek to compare neural and
behavioral measures of reward seeking and loss avoidance across tasks and participants using computational
models and functional MRI in a large sample of participants. Second, we seek to examine individual
differences in reward seeking and loss avoidance learning and their relationship to dimensions of psychiatric
symptomatology using a large online sample. Both aims make use of two parallel and complementary
experimental tasks which each test reward seeking, loss avoidance, and the extent to which the balance
between the two is affected by differences in baseline expectations of reward or loss. Together, these studies
offer an integrative computational framework to test the construct validity of measures of reward seeking and
loss avoidance, the relationship...

## Key facts

- **NIH application ID:** 10449209
- **Project number:** 5R01MH121093-04
- **Recipient organization:** COLUMBIA UNIV NEW YORK MORNINGSIDE
- **Principal Investigator:** Nathaniel Douglass Daw
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $526,636
- **Award type:** 5
- **Project period:** 2019-09-10 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10449209

## Citation

> US National Institutes of Health, RePORTER application 10449209, Differentiating reward seeking and loss avoidance with reference-dependent learning models (5R01MH121093-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10449209. Licensed CC0.

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