# Decoding the dynamic representation of reward predictions across mesocorticostriatal circuits during learning

> **NIH NIH R01** · PRINCETON UNIVERSITY · 2021 · $283,500

## Abstract

Reward learning is a fundamental cognitive function, and the brain has a dedicated neuromodulatory
system – based on dopamine – that supports this process. Changes to the dopamine system that are
triggered by exposure to drugs of abuse are thought to underlie the behavioral changes observed in
addiction. Here we propose to use a treasure trove of previously recorded neural data from
throughout the mesocorticostriatal circuitry that supports reward learning, to elucidate the
computational role of each component of the circuit, their interactions, and how these components
are affected by cocaine.
Our brains constantly generate predictions about what rewards might be available, and compare
these predictions to actual outcomes. The neuromodulator dopamine is thought to report these
‘prediction error’ signals, the result of the ongoing comparison between expected and obtained
rewards, that are key to updating predictions so they are more accurate in the future. Predicting the
timing of rewards, and not just their identity or value, is an important component of this process, but it
remains a mystery how the brain forms and uses predictions about time in reward learning.
Based on a novel theoretical model we recently developed, we will test the computational role of
three key brain areas that comprise the brain circuit critical for reward learning, using a state-of-the-
art methods from machine learning to jointly decode the learning processes that drive neural activity
from multiple brain areas along with behavior as rats perform a reward learning task. In Aim 1, we
hypothesize that neural activity in the orbitofrontal cortex is uniquely important for representing high
level ‘task states’ and will test for patterns in OFC neural activity that follow the hidden structure of the
task. In Aim 2, we will decode the representation of reward predictions about the amount and timing
of rewards, and test whether they are separable in VS neural activity. In Aim 3, we will test how
activity in VS and OFC controls dopamine activity, and in particular how each input component
enables prediction errors to be temporally precise. In Aim 4, we will test how exposure to cocaine
changes neural activity that represents reward predictions in the VS, and the impact of this disruption
on dopamine prediction errors in the VTA.
This innovative multi-level study will leverage numerous existing neural and behavioral data from rats
performing a well-validated reward-learning task, to reveal the computational, neural and behavioral
mechanisms of the reward prediction and learning circuitry in the brain, and the source of their
disruption in addiction.

## Key facts

- **NIH application ID:** 10153745
- **Project number:** 5R01DA050647-02
- **Recipient organization:** PRINCETON UNIVERSITY
- **Principal Investigator:** Yael Niv
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $283,500
- **Award type:** 5
- **Project period:** 2020-05-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10153745

## Citation

> US National Institutes of Health, RePORTER application 10153745, Decoding the dynamic representation of reward predictions across mesocorticostriatal circuits during learning (5R01DA050647-02). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10153745. Licensed CC0.

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