{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T13:47:13Z","timestamp":1787060833826,"version":"3.56.0"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1011713","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T00:00:00Z","timestamp":1703721600000}}],"reference-count":62,"publisher":"Public Library of Science (PLoS)","issue":"12","license":[{"start":{"date-parts":[[2023,12,11]],"date-time":"2023-12-11T00:00:00Z","timestamp":1702252800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000028","name":"Semiconductor Research Corporation","doi-asserted-by":"publisher","award":["2018-JU-2777"],"award-info":[{"award-number":["2018-JU-2777"]}],"id":[{"id":"10.13039\/100000028","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000893","name":"Simons Foundation","doi-asserted-by":"publisher","award":["SCGB-542965"],"award-info":[{"award-number":["SCGB-542965"]}],"id":[{"id":"10.13039\/100000893","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100019335","name":"McGovern Institute for Brain Research, Massachusetts Institute of Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100019335","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>A core problem in visual object learning is using a finite number of images of a new object to accurately identify that object in future, novel images.<\/jats:p>\n                  <jats:p>One longstanding, conceptual hypothesis asserts that this core problem is solved by adult brains through two connected mechanisms: 1) the re-representation of incoming retinal images as points in a fixed, multidimensional neural space, and 2) the optimization of linear decision boundaries in that space, via simple plasticity rules applied to a single downstream layer.<\/jats:p>\n                  <jats:p>Though this scheme is biologically plausible, the extent to which it explains learning behavior in humans has been unclear\u2014in part because of a historical lack of image-computable models of the putative neural space, and in part because of a lack of measurements of human learning behaviors in difficult, naturalistic settings.<\/jats:p>\n                  <jats:p>\n                    Here, we addressed these gaps by 1) drawing from contemporary, image-computable models of the primate ventral visual stream to create a large set of testable learning models (n = 2,408 models), and 2) using online psychophysics to measure human learning trajectories over a varied set of tasks involving novel 3D objects (n = 371,000 trials), which we then used to develop (and\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/www.github.com\/himjl\/hobj\" xlink:type=\"simple\">publicly release<\/jats:ext-link>\n                    ) empirical benchmarks for comparing learning models to humans.\n                  <\/jats:p>\n                  <jats:p>We evaluated each learning model on these benchmarks, and found those based on deep, high-level representations from neural networks were surprisingly aligned with human behavior. While no tested model explained the entirety of replicable human behavior, these results establish that rudimentary plasticity rules, when combined with appropriate visual representations, have high explanatory power in predicting human behavior with respect to this core object learning problem.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1011713","type":"journal-article","created":{"date-parts":[[2023,12,11]],"date-time":"2023-12-11T13:34:35Z","timestamp":1702301675000},"page":"e1011713","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["How well do rudimentary plasticity rules predict adult visual object learning?"],"prefix":"10.1371","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2576-6059","authenticated-orcid":true,"given":"Michael J.","family":"Lee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1592-5896","authenticated-orcid":true,"given":"James J.","family":"DiCarlo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2023,12,11]]},"reference":[{"issue":"4820","key":"pcbi.1011713.ref001","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1126\/science.3629243","article-title":"Toward a universal law of generalization for psychological science","volume":"237","author":"RN Shepard","year":"1987","journal-title":"Science"},{"issue":"2","key":"pcbi.1011713.ref002","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1037\/0033-295X.94.2.115","article-title":"Recognition-by-components: a theory of human image understanding","volume":"94","author":"I Biederman","year":"1987","journal-title":"Psychol Rev"},{"issue":"1","key":"pcbi.1011713.ref003","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1146\/annurev.ps.43.020192.000325","article-title":"Similarity scaling and cognitive process models","volume":"43","author":"RM Nosofsky","year":"1992","journal-title":"Annu Rev Psychol"},{"issue":"4","key":"pcbi.1011713.ref004","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1017\/S0140525X01000061","article-title":"Generalization, similarity, and Bayesian inference","volume":"24","author":"JB Tenenbaum","year":"2001","journal-title":"Behav Brain Sci"},{"key":"pcbi.1011713.ref005","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1146\/annurev.psych.56.091103.070217","article-title":"Human category learning","volume":"56","author":"FG Ashby","year":"2005","journal-title":"Annu Rev Psychol"},{"issue":"3","key":"pcbi.1011713.ref006","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1016\/0010-0285(72)90014-X","article-title":"Pattern recognition and categorization","volume":"3","author":"SK Reed","year":"1972","journal-title":"Cogn Psychol"},{"key":"pcbi.1011713.ref007","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1037\/0033-295X.99.1.22","article-title":"ALCOVE: an exemplar-based connectionist model of category learning","volume":"99","author":"JK Kruschke","year":"1992","journal-title":"Psychol Rev"},{"issue":"1","key":"pcbi.1011713.ref008","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1073\/pnas.89.1.60","article-title":"Psychophysical support for a two-dimensional view interpolation theory of object recognition","volume":"89","author":"HH B\u00fclthoff","year":"1992","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"2","key":"pcbi.1011713.ref009","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1037\/0096-1523.22.2.294","article-title":"Selective attention and the formation of linear decision boundaries","volume":"22","author":"SC McKinley","year":"1996","journal-title":"J Exp Psychol Hum Percept Perform"},{"key":"pcbi.1011713.ref010","doi-asserted-by":"crossref","first-page":"49","DOI":"10.3758\/BF03211715","article-title":"Comparing decision bound and exemplar models of categorization","volume":"53","author":"WT Maddox","year":"1993","journal-title":"Percept & Psychophys"},{"issue":"6255","key":"pcbi.1011713.ref011","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1038\/343263a0","article-title":"A network that learns to recognize three-dimensional objects","volume":"343","author":"T Poggio","year":"1990","journal-title":"Nature"},{"key":"pcbi.1011713.ref012","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1023\/A:1008102413960","article-title":"Visual recognition and categorization on the basis of similarities to multiple class prototypes","volume":"33","author":"S Duvdevani-Bar","year":"1999","journal-title":"Int J Comput Vis"},{"issue":"4","key":"pcbi.1011713.ref013","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1109\/TPAMI.2006.79","article-title":"One-shot learning of object categories","volume":"28","author":"FF Li","year":"2006","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"8","key":"pcbi.1011713.ref014","doi-asserted-by":"crossref","first-page":"1958","DOI":"10.1109\/TPAMI.2012.269","article-title":"Learning with hierarchical-deep models","volume":"35","author":"R Salakhutdinov","year":"2013","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"6266","key":"pcbi.1011713.ref015","doi-asserted-by":"crossref","first-page":"1332","DOI":"10.1126\/science.aab3050","article-title":"Human-level concept learning through probabilistic program induction","volume":"350","author":"BM Lake","year":"2015","journal-title":"Science"},{"issue":"6","key":"pcbi.1011713.ref016","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1037\/rev0000086","article-title":"Visual shape perception as Bayesian inference of 3D object-centered shape representations","volume":"124","author":"G Erdogan","year":"2017","journal-title":"Psychol Rev"},{"issue":"43","key":"pcbi.1011713.ref017","doi-asserted-by":"crossref","first-page":"e2200800119","DOI":"10.1073\/pnas.2200800119","article-title":"Neural representational geometry underlies few-shot concept learning","volume":"119","author":"B Sorscher","year":"2022","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"33","key":"pcbi.1011713.ref018","doi-asserted-by":"crossref","first-page":"7255","DOI":"10.1523\/JNEUROSCI.0388-18.2018","article-title":"Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks","volume":"38","author":"R Rajalingham","year":"2018","journal-title":"J Neurosci"},{"key":"pcbi.1011713.ref019","unstructured":"Geirhos R, Narayanappa K, Mitzkus B, Thieringer T, Bethge M, Wichmann FA, et al. Partial success in closing the gap between human and machine vision. In: Adv Neural Inf Process Syst. vol. 34; 2021. p. 23885\u201323899."},{"key":"pcbi.1011713.ref020","doi-asserted-by":"crossref","first-page":"e82580","DOI":"10.7554\/eLife.82580","article-title":"THINGS-data, a multimodal collection of large-scale datasets for investigating object representations in human brain and behavior","volume":"12","author":"MN Hebart","year":"2023","journal-title":"eLife"},{"key":"pcbi.1011713.ref021","first-page":"2568","article-title":"One shot learning of simple visual concepts","volume":"vol. 33","author":"B Lake","year":"2011","journal-title":"Proc Annu Meet Cogn Sci Soc"},{"key":"pcbi.1011713.ref022","article-title":"The geometry of concept learning","author":"B Sorscher","year":"2021","journal-title":"bioRxiv"},{"issue":"39","key":"pcbi.1011713.ref023","doi-asserted-by":"crossref","first-page":"13402","DOI":"10.1523\/JNEUROSCI.5181-14.2015","article-title":"Simple learned weighted sums of inferior temporal neuronal firing rates accurately predict human core object recognition performance","volume":"35","author":"NJ Majaj","year":"2015","journal-title":"J Neurosci"},{"issue":"5","key":"pcbi.1011713.ref024","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1038\/nn.2304","article-title":"Reinforcement learning can account for associative and perceptual learning on a visual-decision task","volume":"12","author":"CT Law","year":"2009","journal-title":"Nat Neurosci"},{"issue":"3","key":"pcbi.1011713.ref025","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.jmp.2008.12.005","article-title":"Reinforcement learning in the brain","volume":"53","author":"Y Niv","year":"2009","journal-title":"J Math Psychol"},{"key":"pcbi.1011713.ref026","doi-asserted-by":"crossref","first-page":"85","DOI":"10.3389\/fncir.2015.00085","article-title":"Neuromodulated spike-timing-dependent plasticity, and theory of three-factor learning rules","volume":"9","author":"N Fr\u00e9maux","year":"2016","journal-title":"Front Neural Circuits"},{"issue":"6","key":"pcbi.1011713.ref027","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1037\/h0042519","article-title":"The perceptron: a probabilistic model for information storage and organization in the brain","volume":"65","author":"F Rosenblatt","year":"1958","journal-title":"Psychol Rev"},{"issue":"6","key":"pcbi.1011713.ref028","doi-asserted-by":"crossref","first-page":"e1008981","DOI":"10.1371\/journal.pcbi.1008981","article-title":"An image-computable model of human visual shape similarity","volume":"17","author":"Y Morgenstern","year":"2021","journal-title":"PLoS Comput Biol"},{"key":"pcbi.1011713.ref029","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.visres.2019.09.005","article-title":"One-shot categorization of novel object classes in humans","volume":"165","author":"Y Morgenstern","year":"2019","journal-title":"Vision Res"},{"key":"pcbi.1011713.ref030","doi-asserted-by":"crossref","first-page":"530","DOI":"10.3758\/s13428-017-0884-8","article-title":"Toward the development of a feature-space representation for a complex natural category domain","volume":"50","author":"RM Nosofsky","year":"2018","journal-title":"Behav Res Methods"},{"key":"pcbi.1011713.ref031","unstructured":"Singh P, Peterson JC, Battleday RM, Griffiths TL. End-to-end deep prototype and exemplar models for predicting human behavior. arXiv:2007.08723v1 [Preprint]. 2020 [cited 2023 June 23]. Available from: https:\/\/arxiv.org\/abs\/2007.08723"},{"key":"pcbi.1011713.ref032","unstructured":"Battleday RM, Peterson JC, Griffiths TL. Modeling human categorization of natural images using deep feature representations. arXiv:1711.04855v1 [Preprint]. 2017 [cited 2023 June 23]. Available from: https:\/\/arxiv.org\/abs\/1711.04855"},{"issue":"8","key":"pcbi.1011713.ref033","doi-asserted-by":"crossref","first-page":"2648","DOI":"10.1111\/cogs.12670","article-title":"Evaluating (and improving) the correspondence between deep neural networks and human representations","volume":"42","author":"JC Peterson","year":"2018","journal-title":"Cogn Sci"},{"issue":"23","key":"pcbi.1011713.ref034","doi-asserted-by":"crossref","first-page":"8619","DOI":"10.1073\/pnas.1403112111","article-title":"Performance-optimized hierarchical models predict neural responses in higher visual cortex","volume":"111","author":"DLK Yamins","year":"2014","journal-title":"Proc Natl Acad Sci U S A"},{"key":"pcbi.1011713.ref035","unstructured":"Kubilius J, Schrimpf M, Kar K, Rajalingham R, Hong H, Majaj N, et al. Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs. In: Adv Neural Inf Process Syst. vol. 32; 2019. p. 12805\u201312816."},{"issue":"3","key":"pcbi.1011713.ref036","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1016\/j.neuron.2020.07.040","article-title":"Integrative benchmarking to advance neurally mechanistic models of human intelligence","volume":"108","author":"M Schrimpf","year":"2020","journal-title":"Neuron"},{"key":"pcbi.1011713.ref037","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. ImageNet: A large-scale hierarchical image database. In: 2009 Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit; 2009. p. 248\u2013255.","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"5","key":"pcbi.1011713.ref038","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1162\/089976603765202622","article-title":"Estimating a state-space model from point process observations","volume":"15","author":"AC Smith","year":"2003","journal-title":"Neural Comput"},{"key":"pcbi.1011713.ref039","doi-asserted-by":"crossref","first-page":"e253","DOI":"10.1017\/S0140525X16001837","article-title":"Building machines that learn and think like people","volume":"40","author":"BM Lake","year":"2017","journal-title":"Behav Brain Sci"},{"key":"pcbi.1011713.ref040","unstructured":"Marcus G. Deep learning: a critical appraisal; arXiv:1801.00631v1 [Preprint]. 2018 [cited 2023 June 23]. Available from: https:\/\/arxiv.org\/abs\/1801.00631"},{"issue":"6428","key":"pcbi.1011713.ref041","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1126\/science.aau6595","article-title":"Using neuroscience to develop artificial intelligence","volume":"363","author":"S Ullman","year":"2019","journal-title":"Science"},{"issue":"1","key":"pcbi.1011713.ref042","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1038\/s41583-020-00395-8","article-title":"If deep learning is the answer, what is the question?","volume":"22","author":"A Saxe","year":"2021","journal-title":"Nat Rev Neurosci"},{"issue":"6","key":"pcbi.1011713.ref043","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1038\/s41583-020-0277-3","article-title":"Backpropagation and the brain","volume":"21","author":"TP Lillicrap","year":"2020","journal-title":"Nat Rev Neurosci"},{"issue":"1","key":"pcbi.1011713.ref044","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.tics.2009.11.002","article-title":"The neural basis of visual object learning","volume":"14","author":"HP Op de Beeck","year":"2010","journal-title":"Trends Cogn Sci"},{"issue":"11","key":"pcbi.1011713.ref045","doi-asserted-by":"crossref","first-page":"2941","DOI":"10.1523\/JNEUROSCI.3401-04.2005","article-title":"The roles of the caudate nucleus in human classification learning","volume":"25","author":"CA Seger","year":"2005","journal-title":"J Neurosci"},{"key":"pcbi.1011713.ref046","first-page":"120","article-title":"Separate groups of dopamine neurons innervate caudate head and tail encoding flexible and stable value memories","volume":"8","author":"HF Kim","year":"2014","journal-title":"Front Neuroanat"},{"issue":"1","key":"pcbi.1011713.ref047","doi-asserted-by":"crossref","first-page":"1411","DOI":"10.1038\/s41598-019-57261-6","article-title":"Scale and translation-invariance for novel objects in human vision","volume":"10","author":"Y Han","year":"2020","journal-title":"Sci Rep"},{"key":"pcbi.1011713.ref048","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.chb.2014.11.004","article-title":"A reliability analysis of Mechanical Turk data","volume":"43","author":"SV Rouse","year":"2015","journal-title":"Comput Hum Behav"},{"issue":"1","key":"pcbi.1011713.ref049","doi-asserted-by":"crossref","first-page":"1872","DOI":"10.1038\/s41467-021-22078-3","article-title":"Qualitative similarities and differences in visual object representations between brains and deep networks","volume":"12","author":"G Jacob","year":"2021","journal-title":"Nat Commun"},{"issue":"8","key":"pcbi.1011713.ref050","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.tics.2010.05.004","article-title":"Probabilistic models of cognition: exploring representations and inductive biases","volume":"14","author":"TL Griffiths","year":"2010","journal-title":"Trends Cogn Sci"},{"issue":"3","key":"pcbi.1011713.ref051","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1016\/0010-0285(76)90013-X","article-title":"Basic objects in natural categories","volume":"8","author":"E Rosch","year":"1976","journal-title":"Cogn Psychol"},{"issue":"35","key":"pcbi.1011713.ref052","doi-asserted-by":"crossref","first-page":"12127","DOI":"10.1523\/JNEUROSCI.0573-15.2015","article-title":"Comparison of object recognition behavior in human and monkey","volume":"35","author":"R Rajalingham","year":"2015","journal-title":"J Neurosci"},{"key":"pcbi.1011713.ref053","volume-title":"Neuroscience","author":"D Purves","year":"2001","edition":"2"},{"issue":"36","key":"pcbi.1011713.ref054","doi-asserted-by":"crossref","first-page":"13124","DOI":"10.1073\/pnas.0404965101","article-title":"The learning curve: implications of a quantitative analysis","volume":"101","author":"CR Gallistel","year":"2004","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"1","key":"pcbi.1011713.ref055","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1037\/h0062474","article-title":"The formation of learning sets","volume":"56","author":"HF Harlow","year":"1949","journal-title":"Psychol Rev"},{"issue":"5","key":"pcbi.1011713.ref056","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1017\/S1930297500002205","article-title":"Running experiments on Amazon Mechanical Turk","volume":"5","author":"G Paolacci","year":"2010","journal-title":"Judgm Decis Mak"},{"key":"pcbi.1011713.ref057","volume-title":"Evolutionary art and computers","author":"S Todd","year":"1992"},{"key":"pcbi.1011713.ref058","unstructured":"Persistence of Vision Pty. Ltd. Persistence of Vision Raytracer; 2004. Available from: http:\/\/www.povray.org."},{"issue":"1","key":"pcbi.1011713.ref059","doi-asserted-by":"crossref","first-page":"137","DOI":"10.3758\/BF03207704","article-title":"Calculation of signal detection theory measures","volume":"31","author":"H Stanislaw","year":"1999","journal-title":"Behav Res Methods Instrum Comput"},{"key":"pcbi.1011713.ref060","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, et al. PyTorch: an imperative style, high-performance deep learning library. In: Adv Neural Inf Process Syst. vol. 32; 2019. p. 8026\u20138037."},{"issue":"2","key":"pcbi.1011713.ref061","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1007\/BF02764938","article-title":"Extensions of Lipschitz maps into Banach spaces","volume":"54","author":"WB Johnson","year":"1986","journal-title":"Isr J Math"},{"key":"pcbi.1011713.ref062","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9781107298019","volume-title":"Understanding machine learning: from theory to algorithms","author":"S Shalev-Shwartz","year":"2014"}],"updated-by":[{"DOI":"10.1371\/journal.pcbi.1011713","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T00:00:00Z","timestamp":1703721600000}}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1011713","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T13:32:27Z","timestamp":1703770347000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1011713"}},"subtitle":[],"editor":[{"given":"Tim Christian","family":"Kietzmann","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2023,12,11]]},"references-count":62,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,12,11]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1011713","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2022.12.31.522402","asserted-by":"object"}]},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,11]]}}}