# Representation of attentional priority for visual features in the human brain

> **NIH NIH R01** · MICHIGAN STATE UNIVERSITY · 2024 · $384,434

## Abstract

PROJECT SUMMARY
The environment contains far more information than the brain can process at once. Visual attention
helps us cope with such information overload by selectively processing relevant information. In many
situations, humans need to select arbitrary features in a scene. Theories of attention have proposed
that such selection is mediated by a priority representation that encodes the relative importance of
each visual stimulus in the scene. However, much remains unknown regarding how the brain
computes and maintains attentional priority for features. Our long-term goal is to understand how the
brain selects different types of information via population neural activity to serve goal-directed
behavior. In this project, we will examine the neural basis of two basic properties of feature attention:
its resolution and capacity. We hypothesize that distinct areas in the dorsal frontoparietal network
encode priority information with different resolution and capacity limit, supported by distinct neural
population activity profiles. We will test this overall hypothesis by pursuing three specific aims. First,
we will establish functional specializations in frontoparietal areas in representing feature priority with
different levels of resolution. Second, we will examine the nature of priority signals that gives rise to
the capacity limit in attending to multiple stimuli. Third, we will quantify the dimensionality of priority
signals and examine how neural dimensionality determines the resolution and capacity of the priority
representation. The proposed research is expected to significantly advance our understanding of how
the brain selects visual features, in terms of the neural machinery and computational principles that
enable such selection. A deeper understanding of how the brain selects visual features will provide
important constraints for theories and models of attention and can potentially transform our
understanding of visual information processing and cognitive control. The research project is
innovative both in terms of conceptual and methodological advances. Conceptually, the project will
test novel hypotheses regarding the functional dissociations in frontoparietal cortex and the
underlying computational principles of neural coding. Methodologically, the project employs a multi-
modal approach including behavioral, neuroimaging, and neuroperturbation techniques,
complemented by advanced data analytical and computational modeling methods, to gain
fundamental insights into the brain mechanisms of visual attention.

## Key facts

- **NIH application ID:** 10872185
- **Project number:** 5R01EY032071-03
- **Recipient organization:** MICHIGAN STATE UNIVERSITY
- **Principal Investigator:** Taosheng Liu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $384,434
- **Award type:** 5
- **Project period:** 2022-09-30 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10872185

## Citation

> US National Institutes of Health, RePORTER application 10872185, Representation of attentional priority for visual features in the human brain (5R01EY032071-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10872185. Licensed CC0.

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