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Graph theoretic measures of individual category experiences for individual children reveal a \u201clumpy\u201d mix of high similarity and high variability, organized into multiple but interconnected clusters of high-similarity images. In computational experiments, we show that artificially created training sets characterized by an interconnected mix of high and low similarity support generalization to novel instances after limited training. We discuss implications for category recognition, and for learning more generally, by both humans and machines.<\/jats:p>","DOI":"10.1073\/pnas.2536143123","type":"journal-article","created":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T16:24:01Z","timestamp":1786551841000},"update-policy":"https:\/\/doi.org\/10.1073\/pnas.cm10313","source":"Crossref","is-referenced-by-count":1,"title":["A solution to generalized learning from small training sets found in infants\u2019 repeated visual experiences of individual objects"],"prefix":"10.1073","volume":"123","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-2463-6515","authenticated-orcid":false,"given":"Frangil M.","family":"Ramirez","sequence":"first","affiliation":[{"name":"Department of Computer Science, Luddy School of Informatics, Computing, and Engineering, Indiana University","place":["Bloomington"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elizabeth M.","family":"Clerkin","sequence":"additional","affiliation":[{"name":"Department of Psychological and Brain Sciences, Indiana University","place":["Bloomington"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5827-5344","authenticated-orcid":false,"given":"David J.","family":"Crandall","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Luddy School of Informatics, Computing, and Engineering, Indiana University","place":["Bloomington"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7163-8181","authenticated-orcid":false,"given":"Linda B.","family":"Smith","sequence":"additional","affiliation":[{"name":"Department of Psychological and Brain Sciences, Indiana University","place":["Bloomington"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"341","published-online":{"date-parts":[[2026,8,12]]},"reference":[{"key":"e_1_3_4_1_2","unstructured":"D. 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(Lawrence Erlbaum Associates, 1978), pp. 27\u201348."},{"key":"e_1_3_4_34_2","doi-asserted-by":"publisher","DOI":"10.3758\/s13423-014-0585-6"},{"key":"e_1_3_4_35_2","volume-title":"Human Behavior and The Principle of Least Effort","author":"Zipf G. K.","year":"1949","unstructured":"G. K. Zipf, Human Behavior and The Principle of Least Effort (Addison-Wesley Press, 1949)."},{"key":"e_1_3_4_36_2","doi-asserted-by":"publisher","DOI":"10.1037\/0033-295X.84.4.327"},{"key":"e_1_3_4_37_2","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.1460-23.2023"},{"key":"e_1_3_4_38_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41562-020-00951-3"},{"key":"e_1_3_4_39_2","doi-asserted-by":"publisher","DOI":"10.1068\/p7063"},{"key":"e_1_3_4_40_2","first-page":"586","article-title":"The unreasonable effectiveness of deep features as a perceptual metric","author":"Zhang R.","year":"2018","unstructured":"R. Zhang, P. Isola, A. A. Efros, E. Shechtman, O. Wang, \u201cThe unreasonable effectiveness of deep features as a perceptual metric\u201d in IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2018), pp 586\u2013595.","journal-title":"IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)"},{"key":"e_1_3_4_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2013.2251645"},{"key":"e_1_3_4_42_2","doi-asserted-by":"crossref","unstructured":"S. Fu \u201cDreamSim: Learning new dimensions of human visual similarity using synthetic data\u201d in Advances in Neural Information Processing Systems 36 (NeurIPS 2023) (2023) vol. 36.","DOI":"10.52202\/075280-2208"},{"key":"e_1_3_4_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2006.871081"},{"key":"e_1_3_4_44_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF00130487"},{"key":"e_1_3_4_45_2","doi-asserted-by":"crossref","unstructured":"J. Domke Y. 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Sci. 291 20241342 (2024).","DOI":"10.1098\/rspb.2024.1342"},{"key":"e_1_3_4_50_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-006-0031-y"},{"key":"e_1_3_4_51_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-008-0137-5"},{"key":"e_1_3_4_52_2","doi-asserted-by":"crossref","unstructured":"V. Hedau D. Hoiem D. Forsyth \u201cThinking inside the box: Using appearance models and context based on room geometry\u201d in Computer Vision\u2014ECCV 2010 (2010) 10.1007\/978-3-642-15567-3_17 Chap. 17 pp. 224\u2013237.","DOI":"10.1007\/978-3-642-15567-3_17"},{"key":"e_1_3_4_53_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2023.103661"},{"key":"e_1_3_4_54_2","doi-asserted-by":"crossref","unstructured":"A. Kar S. Tulsiani J. Carreira J. Malik \u201cA modal completion and size constancy in natural scenes\u201d in IEEE\/CVF International Conference on Computer Vision (ICCV) (2015) pp 127\u2013135.","DOI":"10.1109\/ICCV.2015.23"},{"key":"e_1_3_4_55_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuropsychologia.2014.08.031"},{"key":"e_1_3_4_56_2","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-025-01072-0"},{"key":"e_1_3_4_57_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jml.2025.104624"},{"key":"e_1_3_4_58_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tics.2007.09.009"},{"key":"e_1_3_4_59_2","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-psych-122414-033634"},{"key":"e_1_3_4_60_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.physrep.2009.11.002"},{"key":"e_1_3_4_61_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0808904106"},{"key":"e_1_3_4_62_2","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0316141","article-title":"Backbone extraction through statistical edge filtering: A comparative study","volume":"20","author":"Mastrandrea R.","year":"2025","unstructured":"R. Mastrandrea, A. Yassin, H. Cherifi, H. Seba, O. Togni, Backbone extraction through statistical edge filtering: A comparative study. Plos One 20, e0316141 (2025).","journal-title":"Plos One"},{"key":"e_1_3_4_63_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2019.116443"},{"key":"e_1_3_4_64_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tins.2020.09.006"},{"key":"e_1_3_4_65_2","doi-asserted-by":"publisher","DOI":"10.1093\/acprof:oso\/9780198506263.001.0001"},{"key":"e_1_3_4_66_2","volume-title":"Graduate Texts in Mathematics","author":"Diestel R.","year":"2025","unstructured":"R. Diestel, \u201cGraph theory\u201d in Graduate Texts in Mathematics (Springer Berlin Heidelberg, ed. 6, 2025). https:\/\/doi.org\/10.1007\/978-3-662-70107-2.","edition":"6"},{"key":"e_1_3_4_67_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0012-365X(01)00180-7"},{"key":"e_1_3_4_68_2","doi-asserted-by":"publisher","DOI":"10.1016\/0885-2014(88)90011-1"},{"key":"e_1_3_4_69_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuron.2019.12.002"},{"key":"e_1_3_4_70_2","unstructured":"A. X. Chang ShapeNet: An Information-Rich 3D Model Repository. arXiv [Preprint] (2015). http:\/\/dx.doi.org\/10.48550\/arXiv.1512.03012 (Accessed June 2024)."},{"key":"e_1_3_4_71_2","doi-asserted-by":"crossref","unstructured":"R. Shinoda K. Shiohara \u201cPetFace: A large-scale dataset and benchmark for animal identification\u201d in Computer Vision\u2014ECCV 2024 (2025) https:\/\/doi.org\/10.1007\/978-3-031-72649-1_2 Chapter 2 pp. 19\u201336.","DOI":"10.1007\/978-3-031-72649-1_2"},{"key":"e_1_3_4_72_2","doi-asserted-by":"crossref","unstructured":"K. M. He X. Y. Zhang S. Q. Ren J. Sun \u201cDeep residual learning for image recognition\u201d in IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2016) pp 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_4_73_2","doi-asserted-by":"crossref","unstructured":"K. Wu \u201cTinyViT: Fast pretraining distillation for small vision transformers\u201d in Computer Vision\u2014ECCV 2022 (2022) https:\/\/doi.org\/10.1007\/978-3-031-19803-8_5. 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Sci."},{"key":"e_1_3_4_78_2","doi-asserted-by":"publisher","DOI":"10.1038\/343263a0"},{"key":"e_1_3_4_79_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuron.2012.01.010"},{"key":"e_1_3_4_80_2","doi-asserted-by":"publisher","DOI":"10.3758\/PBR.15.2.256"},{"key":"e_1_3_4_81_2","doi-asserted-by":"publisher","DOI":"10.1037\/0096-3445.123.2.178"},{"key":"e_1_3_4_82_2","doi-asserted-by":"publisher","DOI":"10.1093\/cercor\/bhs067"},{"key":"e_1_3_4_83_2","doi-asserted-by":"publisher","DOI":"10.1007\/s42113-020-00073-z"},{"key":"e_1_3_4_84_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008102413960"},{"key":"e_1_3_4_85_2","unstructured":"W. W. Sun \u201cCanonical capsules: Self-supervised capsules in canonical pose\u201d in Advances in Neural Information Processing Systems (NeurIPS) (2021) vol. 34."},{"key":"e_1_3_4_86_2","doi-asserted-by":"crossref","unstructured":"J. Wang Y. Chen S. X. Yu \u201cPose-aware self-supervised learning with viewpoint trajectory regularization\u201d in Computer Vision\u2014ECCV 2024. (2025). https:\/\/doi.org\/10.1007\/978-3-031-72664-4_2. Chapter 2 pp. 19\u201337.","DOI":"10.1007\/978-3-031-72664-4_2"},{"key":"e_1_3_4_87_2","doi-asserted-by":"publisher","DOI":"10.1098\/rstb.2007.2154"},{"key":"e_1_3_4_88_2","doi-asserted-by":"publisher","DOI":"10.1121\/1.416296"},{"key":"e_1_3_4_89_2","doi-asserted-by":"publisher","DOI":"10.1121\/1.412280"},{"key":"e_1_3_4_90_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2012.05.008"},{"key":"e_1_3_4_91_2","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553380"},{"key":"e_1_3_4_92_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109347"},{"key":"e_1_3_4_93_2","article-title":"A simple framework for contrastive learning of visual representations","volume":"119","author":"Chen T.","year":"2020","unstructured":"T. Chen, S. Kornblith, M. Norouzi, G. Hinton, A simple framework for contrastive learning of visual representations. Pr. Mach. Learn. Res. 119, 05709 (2020).","journal-title":"Pr. Mach. Learn. Res."},{"key":"e_1_3_4_94_2","doi-asserted-by":"crossref","unstructured":"R. Balestriero L. Bottou Y. LeCun The effects of regularization and data augmentation are class dependent. arXiv [Preprint] (2022). https:\/\/doi.org\/10.48550\/arXiv.2204.03632 (Accessed 1 July 2026).","DOI":"10.52202\/068431-2745"},{"key":"e_1_3_4_95_2","unstructured":"T. Devries G. W. Taylor Improved regularization of convolutional neural networks with cutout. arXiv [Preprint] (2017). http:\/\/dx.doi.org\/10.48550\/arXiv.1708.04552 (Accessed 1 July 2026)."},{"key":"e_1_3_4_96_2","doi-asserted-by":"publisher","DOI":"10.1145\/3510413"},{"key":"e_1_3_4_97_2","unstructured":"G. Zhang M. Cisse Y. Dauphin D. Lopez-Paz \u201cMixup: Beyond empirical risk minimization\u201d in International Conference on Learning Representations (2018)."},{"key":"e_1_3_4_98_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41562-022-01410-x"},{"key":"e_1_3_4_99_2","doi-asserted-by":"publisher","DOI":"10.1177\/00220221251334811"},{"key":"e_1_3_4_100_2","doi-asserted-by":"publisher","DOI":"10.1111\/cdev.13919"},{"key":"e_1_3_4_101_2","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1113\/1\/012007"},{"key":"e_1_3_4_102_2","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.91.062809"},{"key":"e_1_3_4_103_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature03459"},{"key":"e_1_3_4_104_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.aab3050"},{"key":"e_1_3_4_105_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuron.2019.08.034"},{"key":"e_1_3_4_106_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cognition.2016.03.005"},{"key":"e_1_3_4_107_2","doi-asserted-by":"publisher","DOI":"10.1016\/0166-4328(83)90158-4"},{"key":"e_1_3_4_108_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0166-4328(05)80197-4"},{"key":"e_1_3_4_109_2","doi-asserted-by":"publisher","DOI":"10.1080\/15250000802004437"},{"key":"e_1_3_4_110_2","doi-asserted-by":"publisher","DOI":"10.1177\/0165025413495749"},{"key":"e_1_3_4_111_2","doi-asserted-by":"crossref","unstructured":"S. Bambach L. B. Smith D. J. Crandall C. Yu \u201cObjects in the center: How the infant\u2019s body constrains infant scenes\u201d in 2016 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) (2016) pp 132\u2013137.","DOI":"10.1109\/DEVLRN.2016.7846804"},{"key":"e_1_3_4_112_2","doi-asserted-by":"publisher","DOI":"10.1167\/jov.24.10.974"},{"key":"e_1_3_4_113_2","unstructured":"A. Paszke \u201cPyTorch: An imperative style high-performance deep learning library\u201d in 2019 Advances in Neural Information Processing Systems 32 (NeurIPS) (2019) vol. 32."},{"key":"e_1_3_4_114_2","unstructured":"R. Wightman PyTorch Image Models. GitHub. https:\/\/github.com\/rwightman. Accessed 1 March 2026."},{"key":"e_1_3_4_115_2","unstructured":"L. B. Smith HomeView1 Mealtime Egocentric Images. Databrary. https:\/\/databrary.org\/volume\/1965. Deposited 27 July 2026."},{"key":"e_1_3_4_116_2","unstructured":"F. M. Ramirez E. M. Clerkin D. J. Crandall L. B. 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