Understanding how people perceive different materials
Official title: Learning diagnostic latent representations for human material perception: common mechanisms and individual variability
Funded at American University · NIH-10580295 · Data updated 2026-11-12
This study is exploring how people tell different materials apart, like plastic from glass, and it’s using smart computer technology to help figure out how things like light and shape influence our ability to recognize these materials.
Quick facts
| Grant type | R15 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | American University NIH-funded |
| Lab location | 1 site (Washington, UNITED STATES) |
| Project start | April 1, 2023 |
| Project end | March 31, 2026 |
| Funding year | 2023 |
| Award (this year) | $420,538 |
| NIH project number | 1R15EY033512-01A1 |
| Project ID | NIH-10580295 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals with visual impairments who may benefit from advancements in prosthetic vision technology.
✕Less likely to help
Patients with no visual impairments or those who do not use ocular prosthetics may not receive direct benefits from this research.
What this research studies
This research investigates how humans identify and discriminate between various materials, such as distinguishing between plastic and glass. It aims to uncover the underlying mechanisms of material perception by using advanced artificial intelligence techniques, specifically unsupervised deep learning models. The study will analyze how different factors, like lighting and shape, affect our ability to recognize materials, and will involve training AI models on real-world images to better understand these processes. By combining insights from psychology and machine learning, the research seeks to improve our understanding of how we perceive the world around us.
Why it matters
Potential benefit: If successful, this research could enhance the development of artificial vision systems and improve the design of ocular prosthetics, leading to better quality of life for individuals with visual impairments.
How similar studies have gone: While the approach of combining AI with human perception studies is innovative, similar research has shown promise in improving machine learning models for visual recognition tasks.
Where this research is happening
-
American University
Washington, United States Active
Researchers
- Principal investigator: Xiao, Bei — American University
- Study coordinator: Xiao, Bei
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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.