Bruce C. Hansen - US grants
Affiliations: | Colgate University, Hamilton, NY, United States |
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The funding information displayed below comes from the NIH Research Portfolio Online Reporting Tools and the NSF Award Database.The grant data on this page is limited to grants awarded in the United States and is thus partial. It can nonetheless be used to understand how funding patterns influence mentorship networks and vice-versa, which has deep implications on how research is done.
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High-probability grants
According to our matching algorithm, Bruce C. Hansen is the likely recipient of the following grants.Years | Recipients | Code | Title / Keywords | Matching score |
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2013 — 2016 | Ho, Arnold Kelly, Spencer (co-PI) [⬀] Hansen, Bruce Johnson, Douglas (co-PI) [⬀] Keating, Caroline |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
@ Colgate University With support from the Major Research Instrumentation Program, Dr. Bruce C Hansen and his collaborators will purchase a state-of-the-art electroencephalography (EEG) system from Electrical Geodesics Incorporated (GES 300 system) for shared use by faculty and undergraduate students in Colgate University's Department of Psychology and Neuroscience Program. The EEG technique itself involves placing surface electrodes on the scalp of a human participant and recording electrical signals generated by the brain in real-time, thereby allowing for a wide variety of analyses focused on the temporal localization of different brain signals. The system will enable this group to adopt an integrated model for understanding human behavior by blending traditional psychological methodology with functional neuroelectric activity in humans. |
0.915 |
2017 — 2020 | Hansen, Bruce | N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
@ Colgate University A long-standing problem in cognitive neuroscience is understanding how we can categorize a novel scene in about the same amount of time that it takes to blink one's eyes. Categorization aids both identifying objects and locating them in cluttered scenes, and thus allows for intelligent action in the world. How do we derive semantically meaningful categories from the raw image pixels? Currently, there is experimental support for multiple mechanisms supporting scene categorization, such as through recognizing the scene's objects or other visual features such as spatial layout, color, or texture. Crucially, substantial correlations exist between all of these proposed features. This make it difficult to disentangle their relative contributions to categorization. For example, if two scenes share an object, they will often also share the texture features associated with that object. In this work, the PI (Dr. Bruce C Hansen, Colgate University) and co-PI (Dr. Michelle R Greene, Bates College) seek to disentangle the contribution of such features, and also to determine when these features become available for use, and how they combine to support scene categorization. By understanding the temporal dynamics of the brain activity related to scene categorization, it will be possible to obtain critical insights into how people rapidly but flexibly extract information from the environment. This work forms a bridge across several disciplines including psychology, cognitive neuroscience, computer vision, and machine learning. As such, the project will engage undergraduate students in truly interdisciplinary training that is at the cutting edge of multiple fields. |
0.915 |