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. 2023 Oct 16;26(11):2017–2034. doi: 10.1038/s41593-023-01442-0

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© The Author(s) 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Extended Data Fig. 2 — a, 16-way confusion matrix for natural images. Here and in b and c, results incorporate human responses from all experiments that contained the AlexNet Standard architecture or the ResNet50 Standard architecture (N = 104 participants). Statistical test for confusion matrix described in b. b, Confusion matrices for human recognition judgments of model metamers from each stage of the AlexNet and ResNet50 models (using data from all experiments that contained the AlexNet Standard architecture or the ResNet50 Standard architecture). We performed a split-half reliability analysis of the confusion matrices to determine whether the confusions were reliable across participants. We measured the correlation between the confusion matrices for splits of human participants, and assessed whether this correlation was significantly greater than 0 (one-sided test). P-values from this analysis are given above each confusion matrix. For the later stages of each model, the confusion matrices are no more consistent than would be expected by chance, consistent with the metamers being completely unrecognizable (that is, containing no information about the visual category of the natural image they are matched to). c, Human recognizability of model metamers from different stages of AlexNet (N = 63 participants) and ResNet50 models (N = 84 participants). Error bars are s.e.m. across participants. Stages whose confusions were not consistent across splits of human observers are noted by the shaded region. The stages for which recognition is near chance show inconsistent confusion patterns, ruling out the possibility that the chance levels of recognition are driven by systematic errors (for example consistently recognizing metamers for cats as dogs).