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Object reconstruction as top-down attentional feedback
Through a combination of computational modeling and behavioral experiments, we demonstrate how the process of generating objects—actively reconstructing the most plausible object representation from noisy visual input—guides attention towards specific features or locations within an image (known functions of top-down attention), thereby enhancing the system's robustness to various types of noise and corruption. We found that this generative attention mechanism could explain, not only the time that it took people to recognize challenging objects, but also the types of recognition errors made by people (seeing an object as one thing when it was really another). Ahn et al
Image Credit: Seoyoung Ahn
Citation: (2024) PLoS Computational Biology Issue Image | Vol. 20(6) July 2024. PLoS Comput Biol 20(6): ev20.i06. https://doi.org/10.1371/image.pcbi.v20.i06
Published: July 1, 2024
Copyright: © 2024 . This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Through a combination of computational modeling and behavioral experiments, we demonstrate how the process of generating objects—actively reconstructing the most plausible object representation from noisy visual input—guides attention towards specific features or locations within an image (known functions of top-down attention), thereby enhancing the system's robustness to various types of noise and corruption. We found that this generative attention mechanism could explain, not only the time that it took people to recognize challenging objects, but also the types of recognition errors made by people (seeing an object as one thing when it was really another). Ahn et al
Image Credit: Seoyoung Ahn