Rapid Visual Object Learning in Humans is Explainable by Low-Dimensional Image Representations
How humans learn to recognize new objects is an open problem. In this thesis, we consider one class of theories for how this is accomplished: humans re-represent incoming retinal images in a stable, multidimensional Euclidean space, and build linear decoders in this space for new object categories f...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2023
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| Online Access: | https://hdl.handle.net/1721.1/147557 https://orcid.org/0000-0002-2576-6059 |