# CRCNS: Neurocomputational  Study of Reward-Related Decision-Making & Uncertainty

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2021 · $239,291

## Abstract

Humans and animals often make decisions under uncertainty, whereby each decision affects not only the
immediate reward gain but also longer-term information gain. While important advances have been made
in understanding human learning and decision-making, there is still a lack of understanding of the different
motivational factors that come into play when the behavioral context confers systematically varying
amounts of reward and information gain. This project tackles this problem using a combination of
sophisticated cognitive modeling, innovative behavioral experiments, fMRI data, physiological
(pupillometry, cardiac, and respiratory) data, and psychiatric measures (questionnaires addressing
depressiveness, anxiety, anhedonia, locus of control, pessimism, and substance abuse). The objectives
are (1) to develop a statistically grounded and neurobiologically informed theory for how different
motivational factors (immediate reward, long-term reward, reduction of uncertainties, and random
stochasticity) jointly influence human decision making; (2) use this theoretical framework to guide the
understanding of how different brain regions, in particular neuromodulatory systems, work separately and
conjointly to implement behavioral choices in response to the reward and informational structure of the
environment; (3) characterize individual differences in terms of motivations, subjective monitoring of
uncertainties, neural and physiological responses, and psychiatric profile. This work builds on multiple
theoretic approaches: Bayesian ideal observer, reinforcement learning, Markov decision process, and
control theory; and multiple neuroscientific research areas: learning, information seeking, confidence,
decision making, change-point detection. It will advance an integrated understanding of computational
theory, neuro-cognitive processes, behavioral manifestations, physiological signals, and psychiatric traits
in choice behavior under uncertainty. It will help to clarify how different cortical and subcortical (especially
neuromodulatory) brain regions differentially and cooperatively contribute to reward- and
information-based learning, decision making, and exploration. These outcomes can be expected to
contribute to advancements in basic scientific understanding of brain circuits, mechanisms, and functions
related to the use and abuse of addictive substances, as well as their prevention and treatment.
RELEVANCE (See instructions):
Drug use and abuse often involve alterations in reward learning, decision-making, and uncertainty-related
processing. This project contributes to basic computational and neurobiological understanding of these
processes in the healthy brain, and may help to elucidate how these processes go awry in substance use
and addiction disorders.

## Key facts

- **NIH application ID:** 10246421
- **Project number:** 5R01DA050373-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Angela Yu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $239,291
- **Award type:** 5
- **Project period:** 2019-09-30 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10246421

## Citation

> US National Institutes of Health, RePORTER application 10246421, CRCNS: Neurocomputational  Study of Reward-Related Decision-Making & Uncertainty (5R01DA050373-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10246421. Licensed CC0.

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