Collaborative Research: RUI: HNDS-R: Learning task-relevant visual features by linking lar — NSF Award to Barnard College (NY, $37
Building artificial intelligence (AI) systems that approach human cognitive flexibility requires a better understanding of how the brain uses visual and linguistic information to achieve specific goals. While previous research in cognitive neuroscience and AI has focused on visual classification tasks, such as identify
| Award title | Collaborative Research: RUI: HNDS-R: Learning task-relevant visual features by linking lar |
|---|---|
| Award ID | 2522311 |
| Awardee | Barnard College |
| City | NEW YORK |
| State | NY |
| Amount obligated | $378,352 |
| Principal investigator | Michelle Greene |
| Program | Cognitive Neuroscience, Human Networks & Data Sci Res |
| Start date | 09/01/2025 |
| Abstract | Building artificial intelligence (AI) systems that approach human cognitive flexibility requires a better understanding of how the brain uses visual and linguistic information to achieve specific goals. While previous research in cognitive neuroscience and AI has focused on visual classification tasks, such as identifying objects or labeling scenes, real-world behavior is more nuanced and often depends on selecting task-relevant information, guided by the observer’s goals. Critically, this proce |
| Source | NSF Awards |
$799/mo
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