Understanding how we search for objects in real-world scenes using eye movements
Official title: Foveated Search Model for Real World Scenes
Funded at University of California Santa Barbara · NIH-11081009 · Data updated 2026-11-12
This study is exploring how our eye movements connect to how we pay attention and think, especially when we're looking for things in pictures, and it's designed for anyone interested in understanding how we see and process what we look at in everyday life.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California Santa Barbara NIH-funded |
| Lab location | 1 site (Santa Barbara, United States) |
| Project start | May 1, 2024 |
| Project end | April 30, 2026 |
| Funding year | 2025 |
| Award (this year) | $150,000 |
| NIH project number | 5R21EY036488-02 |
| Project ID | NIH-11081009 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research would include individuals experiencing cognitive impairments or deficits that affect their visual attention and search capabilities.
✕Less likely to help
Patients with stable cognitive function and no visual attention issues may not receive direct benefits from this research.
What this research studies
This research investigates how eye movements relate to attention and cognition, particularly in the context of searching for objects in real-world images. By developing a new computational model called the Foveated Search Transformer Model (FST), the research aims to simulate human-like eye movements and decision-making processes when given simple linguistic instructions. The model will incorporate aspects of human vision, such as how vision degrades at the edges of our sight and the context of objects in a scene, to improve accuracy in visual search tasks. This approach combines advanced AI techniques with insights from cognitive science to better understand and replicate human visual behavior.
Why it matters
Potential benefit: If successful, this research could enhance AI systems used in various applications, leading to improved interactions between humans and machines in tasks requiring visual understanding.
How similar studies have gone: Previous research has shown promise in using computational models to understand eye movements, but this specific approach with the FST model is novel and untested.
Where this research is happening
-
University of California Santa Barbara
Santa Barbara, United States Active
Researchers
- Principal investigator: Eckstein, Miguel Patricio — University of California Santa Barbara
- Study coordinator: Eckstein, Miguel Patricio
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.