# Deep sampling of cognitive effects in the human visual system

> **NIH NIH R01** · UNIVERSITY OF MINNESOTA · 2024 · $466,083

## Abstract

PROJECT SUMMARY
Much progress has been achieved in building computational models that describe how visual stimuli are encoded
and transformed across the hierarchy of visual areas in the primate brain. However, relatively little attention has
been given to the active component of visual processing, whereby the observer’s engagement in a cognitive or
perceptual task can exert substantial influence on stimulus-evoked activity. The long-term goal of the proposed
research is to develop models that account for not only stimulus-driven but also task-driven effects in the visual
system. To achieve this goal, we invest effort in improving analysis methodology and in generating large high-
quality experimental datasets. In Aim 1, we develop and optimize methods that provide robust fMRI
measurements at the level of single trials. This includes a method that exploits temporal dynamics to isolate
BOLD signals that are more closely related to local neural activity, and an algorithm that improves signal-to-
noise ratio in general linear modeling of fMRI data. In Aim 2, we acquire and prepare a high-field (7T) fMRI
dataset in which a large variety of tasks are performed on a common set of visual stimuli. This dataset exploits
deep sampling of a small number of subjects, and will generate a rich, reusable resource for the fields of cognitive
and computational neuroscience. In Aim 3, we exploit the dataset to test an extant model of how top-down
influences from the intraparietal sulcus modulate responses in ventral visual cortex and to assess how top-down
signals from frontal cortex modulate the fidelity of working memory stimulus encoding in visual cortex. Overall,
this multidisciplinary proposal will deliver reusable methods and data resources, and will push computational
modeling from the domain of stimulus representation into better understanding how the visual system mediates
real-world visual perception and task engagement. Advancing our understanding of the computational
mechanisms underlying visual processing in healthy individuals is a critical step for unraveling the nature of
sensory disorders such as prosopagnosia and dyslexia.

## Key facts

- **NIH application ID:** 10862842
- **Project number:** 5R01EY034118-02
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** CLAYTON E CURTIS
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $466,083
- **Award type:** 5
- **Project period:** 2023-07-01 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10862842

## Citation

> US National Institutes of Health, RePORTER application 10862842, Deep sampling of cognitive effects in the human visual system (5R01EY034118-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10862842. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*