# Guiding Attention in Real-World Scenes

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA AT DAVIS · 2022 · $368,858

## Abstract

Project Summary
Real-world scenes contain far more information than we can perceive at any given moment. Scene perception
therefore requires attentional selection of relevant scene regions for prioritized processing. How are those
aspects of the world that should receive priority selected? Although much past research has focused on how
attention is guided by the visual properties of a scene, new evidence from meaning maps, developed in the
previous funding period, established that the distribution of meaning across a scene plays a central and often
dominant role in guiding attention. This surprising finding raises many important new questions about the
nature of scene meaning and its specific role in attentional guidance. The overarching goal of this project is to
understand in detail how the semantic features of a scene’s objects and functional spaces influence the
guidance of visual attention in complex real-world scenes. The specific aims are: (1) To determine the role of
object semantics in attentional guidance in scenes; (2) To determine the role of functional spaces in attentional
guidance in scenes; (3) To determine how viewing task interacts with scene semantics in guiding attention.
The project is innovative in expanding the traditional study of attention to explicitly consider the role of
meaning. To this end, new semantic maps capitalizing on the meaning map concept will be used capture local
region meaning continuously over a scene, allowing for direct investigation of the relationships of different
types of meaning with attention. The project is innovative in (1) expanding the traditional study of visual
attention to explicitly consider the role of semantics; (2) focusing on the semantics of both scene content
(objects) and scene structure (space); (3) considering the role of meaning in attentional guidance in the context
of viewing task; (4) integrating the use of a wide variety of cognitive science methods marshalled in the service
of understanding the influence of meaning on visual attention in real-world scenes, including eyetracking,
large-scale crowd-sourcing, computational image processing, computational semantic modeling, and deep
convolutional neural networks. The project is significant in challenging current models of visual attention to
account for the role of scene meaning. Because the proposed studies test competing models, the results will
lead to the development of integrative theoretical frameworks that advance the field regardless of the outcome.
While focused on basic science, the studies have potentially important translational implications by providing a
more complete characterization of the processes associated with visual attention. The proposed studies may
ultimately lead to the development of rehabilitation strategies for visual attention as it operates in the real
world, better capitalizing on the use of a viewer’s knowledge to offset disrupted functions in those with attention
and vision deficits.

## Key facts

- **NIH application ID:** 10477474
- **Project number:** 5R01EY027792-06
- **Recipient organization:** UNIVERSITY OF CALIFORNIA AT DAVIS
- **Principal Investigator:** John M Henderson
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $368,858
- **Award type:** 5
- **Project period:** 2017-09-01 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10477474

## Citation

> US National Institutes of Health, RePORTER application 10477474, Guiding Attention in Real-World Scenes (5R01EY027792-06). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10477474. Licensed CC0.

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