# An investigation into the neurobehavioral interactions between sensory- and reward-prediction errors during motor learning

> **NIH NIH F32** · PRINCETON UNIVERSITY · 2021 · $65,994

## Abstract

PROJECT SUMMARY
When we experience a prediction error, a difference between what was expected and what was observed, the
brain uses information conveyed by the error to guide adaptive learning that improves future accuracy. The
proposed research focuses on the mechanisms by which concurrently available prediction errors inform
changes in our motor behaviors. Prior work divided prediction errors into two categories, according to the
conditions that cause them and the learning that they induce. Reward-prediction errors (RPE) arise when task
performance is better or worse than expected, and cause learning that affects action selection. Sensory-
prediction errors (SPE), on the other hand, occur when there is a mismatch between a movement’s expected
and observed sensory feedback, and drive adaptation of that particular movement. Prior work has linked RPEs
with the basal ganglia and SPEs with the cerebellum, leading to the idea that the two kinds of error operate on
distinct networks to control distinct learning processes. Indeed, SPE (but not RPE) causes implicit adaptation
beneath the level of conscious awareness, while RPE (but not SPE) causes explicit learning, suggesting that
different neural systems use the different error signals to support separate learning processes. However,
recent work has revealed that joint SPE and RPE produce different effects on implicit and explicit learning than
either in isolation, suggesting that SPE-based and RPE-based learning networks interact. These effects may
be mediated by a direct interaction between SPE and RPE signals, with presentation of one error affecting the
neural teaching signal that normally drives learning in response to the other. Alternately, the interaction may be
indirect, with parallel learning in SPE- and RPE-based systems converging on a common neural target to
influence behavioral output. To understand the mechanism underlying these interactions, I will present different
prediction errors during visuomotor reach adaptation (VMR) and measure their independent and joint effects
on behavior and fMRI BOLD signals. Neuropsychological investigations will complement these studies and
provide insight into the functional interactions between errors and their supporting systems during disease.
These studies, by focusing on the neural basis for interactions between errors and the systems that process
them, will provide valuable insight into the events and signals that support adaptive motor learning. My findings
may also provide insight for the design of rehabilitation protocols based on new knowledge about the capacity
for different error signals to drive learning in systems affected or spared by disease.

## Key facts

- **NIH application ID:** 10283091
- **Project number:** 1F32NS122921-01
- **Recipient organization:** PRINCETON UNIVERSITY
- **Principal Investigator:** Olivia Ann Kim
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $65,994
- **Award type:** 1
- **Project period:** 2021-09-01 → 2024-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10283091

## Citation

> US National Institutes of Health, RePORTER application 10283091, An investigation into the neurobehavioral interactions between sensory- and reward-prediction errors during motor learning (1F32NS122921-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10283091. Licensed CC0.

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