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. 2017 May 22;8:15037. doi: 10.1038/ncomms15037

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Copyright © 2017, The Author(s)

This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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Identification was performed for all combinations of 1 of the 50 test object categories and 1 of the 15,322 candidate categories (identification from two categories; predicted from VC: error bars, 95% CI across five subjects; dashed line, chance level, 50%). (a) Seen object identification accuracy. (b) Imagined object identification accuracy. While the identification analyses here were performed with the image feature decoders trained to predict image features of presented images, it is also possible to use category-average feature decoders trained with category-average features for training stimulus images. See Supplementary Figs 12 and 13 for the results of the category-average feature decoders.