# Using multiple species, stimuli, and tasks to study the neural basis of visually guided behavior

> **NIH NIH K99** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2020 · $120,107

## Abstract

Project Summary
 The visual system must constantly extract behaviorally relevant stimulus information from an abundance of
irrelevant inputs from the environment, using cognitive phenomena such as attention and learning to guide this
continuously adapting process. Understanding the mechanisms by which task-relevant information is extracted
from the high-dimensional activity of neuronal populations will be vital to understanding the complex etiology of
many neurological diseases, such as disorders of attention. A longstanding assumption has been that this
process is optimized for each specific visual task, maximizing the amount of information extracted from the
activity of neuronal populations. While this may be possible in highly reductionist lab settings with simple
stimuli, such specific optimization would be virtually impossible in the face of the abundant and rapidly
changing stimuli and task goals encountered in the natural world. Our recent work suggests a new hypothesis:
the extraction of information from neuronal population activity is optimized not for each specific visual task, but
generally for the wide variety of stimuli and tasks encountered in realistic environments.
 In each of our Aims, we will use feature-rich, realistic visual stimuli, precise psychophysical measurements
of perceptual performance, simultaneous recordings from populations of visual neurons, and cutting edge data
analysis techniques to test one prediction of our central hypothesis. In Aim 1, we will test the prediction that in
realistic environments with changes to both task-relevant and -irrelevant visual features, neuronal information
extraction is optimized generally for all of the encountered feature changes, instead of just for the task-relevant
changes. In Aims 2 and 3, we will test the extent to which our central hypothesis is true across different time
frames. In Aim 2, we will test the prediction that the neuronal information extraction process is optimized to be
flexible on short time scales, in the face of rapidly changing task goals. In Aim 3, we will test the prediction that
information extraction can also be flexibly optimized on long time scales, explaining gradual and highly specific
improvements in perceptual ability due to perceptual learning. The results of these studies will have broad
implications both for biophysical models of visual perception and for our understanding of how neuronal
mechanisms in general are able to flexibly adapt to our constantly changing natural environment.
 The proposed project will not only further our understanding of how neuronal activity guides behavior in
the context of realistic visual environments, but will provide me with the necessary technical and analytical
skills to launch my career as an independent investigator. By receiving expert training to create and operate
complex, feature-rich visual stimuli, to collect precise psychophysical measurements by parametrically varying
specific aspects of those stimuli...

## Key facts

- **NIH application ID:** 10040904
- **Project number:** 1K99NS118117-01
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Amy Meesun Ni
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $120,107
- **Award type:** 1
- **Project period:** 2020-09-15 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10040904

## Citation

> US National Institutes of Health, RePORTER application 10040904, Using multiple species, stimuli, and tasks to study the neural basis of visually guided behavior (1K99NS118117-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10040904. Licensed CC0.

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