# Modeling and mapping multiple computational processes in human reinforcement learning

> **NIH NIH F32** · UNIVERSITY OF CALIFORNIA BERKELEY · 2020 · $20,742

## Abstract

Project Summary
Learning from rewards is one of the fundamental roles of the nervous system, allowing for beneficial behaviors
to be repeated, and detrimental behaviors to be avoided. It has recently become clear that when humans learn
from rewards in the environment, they rely on multiple neural systems that work in tandem. What are the
psychological and biological constraints of these systems, and how do they interact during learning? The
proposed experiments are designed to answer these questions by developing precise computational models of
human instrumental learning, as well as investigating the neural dynamics of, and interactions between,
individual learning processes. Aim 1 will focus on isolating individual learning processes, further developing a
novel model of human instrumental learning that highlights contributions to learning from both a flexible
executive working memory module and an incremental reinforcement learning module. Behavioral experiments
and computational modeling will be used to better characterize these two learning processes, with a focus on
how they interact instantaneously and over time. Aim 2 will use a combination of brain stimulation and
neuroimaging to better characterize the neural systems supporting each learning process, as well as the
putative interactions between these neural circuits. These results will constrain our understanding of the neural
mechanisms that drive human instrumental learning. The knowledge gained by this project will provide a vital
framework for clinical applications, for instance, in understanding and treating working memory related learning
deficits in schizophrenia, and reinforcement learning deficits in Parkinson's disease. Critically, a more precise
model of individual learning processes could guide the development of clinical protocols that leverage intact
learning systems when other learning systems are compromised. Finally, an enhanced understanding of
human reward-based learning could improve theories of habit formation, which may further inform
psychological and neurophysiological models of addiction.

## Key facts

- **NIH application ID:** 9882891
- **Project number:** 5F32MH119797-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA BERKELEY
- **Principal Investigator:** Samuel David McDougle
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $20,742
- **Award type:** 5
- **Project period:** 2019-03-01 → 2020-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9882891

## Citation

> US National Institutes of Health, RePORTER application 9882891, Modeling and mapping multiple computational processes in human reinforcement learning (5F32MH119797-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9882891. Licensed CC0.

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