# Visual-motor integration in smooth pursuit eye movements

> **NIH NIH F30** · DUKE UNIVERSITY · 2020 · $50,520

## Abstract

Abstract
Many behaviors operate in a Bayesian framework, where actions are guided by a complex interaction between
current sensory information and past experience, or “priors”. When current sensory information is weak, it is
advantageous to allow the prior to guide behavior. However, the value of prior experience lessens as sensory
information strengthens. Our goal is to determine how Bayesian behavior arises from the operation of a neural
circuit. Smooth pursuit eye movement is an example of a relatively simple sensory-motor behavior that operates
in a Bayesian-like manner. Smooth pursuit is a visually-guided voluntary eye movement that can be separated
into two primary components: visual drive and visuomotor gain. It is appealing to think of these two components
from a Bayesian perspective: the visual drive arises from the middle temporal visual area (MT) and represents
the likelihood function derived from sensory data; the visuomotor gain is controlled by the smooth pursuit region
of the frontal eye field (FEFsem) and represents the prior. The two concepts and their neural instantiations are
integrated within the pursuit system to drive the ultimate eye movement. Both components have been studied
independently, but our goal is to study their integration. The dorsolateral pontine nucleus (DLPN) and the nucleus
reticularis tegmenti pontis (NRTP) both receive convergent input from cortical areas of pursuit and have cells
with a range of visual and visuomotor signals. Therefore, we will record from single neurons in the DLPN and
NRTP of awake behaving rhesus monkeys to study the integration of visual and visuomotor signals in the pursuit
system. To better understand the relationship between the integration of visual and visuomotor signals and the
emergence of Bayesian-like behavior, we have developed a behavioral paradigm that allows us to rapidly adapt
priors for target speed. We can control tightly the adaptation of the prior by adjusting the statistics of the target
speed, and we can control the expression of the prior by adjusting the strength of the visual motion signals. This
will reveal how the integration of visual and visuomotor signals in the pons changes as a function of the state of
the prior and the strength of sensory information. To directly study the role of FEFsem in the integration of these
signals, we will pair our pontine recordings with simultaneous recordings of multiple single units in the FEFsem.
Neuron-neuron correlations between the responses of pontine and FEFsem neurons will reveal functional
connectivity between the areas and move towards a description of how the pursuit circuit works as a system. By
gaining a better understanding of how the pursuit system integrates priors and sensory information, we will
develop general principles of sensory-motor brain circuits.

## Key facts

- **NIH application ID:** 9873042
- **Project number:** 5F30EY027684-04
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Timothy Darlington
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $50,520
- **Award type:** 5
- **Project period:** 2017-03-01 → 2021-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9873042

## Citation

> US National Institutes of Health, RePORTER application 9873042, Visual-motor integration in smooth pursuit eye movements (5F30EY027684-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9873042. Licensed CC0.

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