# Generalized prediction errors in the human cerebellum

> **NIH NIH R01** · YALE UNIVERSITY · 2024 · $406,188

## Abstract

Project Summary
 In addition to motor control and learning, the cerebellum is intimately linked to cognition. This project is
designed to closely examine the cerebellum's role in nonmotor domains, namely, reinforcement learning and
statistical learning. We hypothesize that the structure's core computations for sensorimotor learning can be
generalized to nonmotor contexts. It is critical to understand how the cerebellum contributes to nonmotor learning
– this knowledge will support the development of novel mechanistic and clinical insights into cerebellar function,
and human learning in general.
 Foundational theoretical work has described how the cerebellum typifies an ideal substrate for supervised
motor learning. This theory made testable empirical predictions that have been borne out in experiments in
animals using tasks such as Pavlovian eyeblink conditioning and vestibular-ocular reflex adaptation, revealing
facts about cerebellar sensorimotor processes in exquisite detail. But what about a cerebellar role in other task
domains? Here we address this question. The proposed work integrates behavioral, neuroimaging, and
computational techniques to develop a new framework for generalized cerebellar learning computations.
 The research plan centers on three Specific Aims. In Aim 1 we use computationally guided functional
neuroimaging (fMRI) to examine the role of the cerebellum in reinforcement learning. We test the idea that the
cerebellum processes reward predictions and prediction errors, the core computations of reinforcement learning.
We also posit a constraint on cerebellar learning computations, namely that the structure only contributes to
learning when the temporal interval between events is brief (i.e., subsecond). Aim 2 takes a similar approach to
the domain of visual statistical learning, examining sensory predictions and prediction errors in the cerebellum
and further testing the proposed timing constraint. In Aims 1-2 we also measure cerebro-cerebellar connectivity
to position the cerebellum within broader task-specific learning networks, and to ask if cerebro-cerebellar
connectivity covaries with behavior. In Aim 3 we examine causal contributions of the cerebellum to nonmotor
learning, testing a large sample of individuals with cerebellar pathology and contrasting their behavior with
matched controls. Computational analyses will be used to detect and characterize the hypothesized deficits. This
project proposes a new framework for understanding the contributions of the cerebellum to nonmotor learning
and will provide new insight into the broader role of the cerebellum in health and disease.

## Key facts

- **NIH application ID:** 10879145
- **Project number:** 5R01NS132926-02
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Samuel David McDougle
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $406,188
- **Award type:** 5
- **Project period:** 2023-07-01 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10879145

## Citation

> US National Institutes of Health, RePORTER application 10879145, Generalized prediction errors in the human cerebellum (5R01NS132926-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10879145. Licensed CC0.

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