Real-world size of objects serves as an axis of object space

Real-world size of objects serves as an axis of object space | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Real-world size of objects serves as an axis of object space Taicheng Huang, Yiying Song, Jia Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1055467/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Jul, 2022 Read the published version in Communications Biology → Version 1 posted You are reading this latest preprint version Abstract Our mind can represent various objects from the physical world metaphorically into an abstract and complex high-dimensional object space, with a finite number of orthogonal axes encoding critical object features. However, little is known about what features serve as axes of the object space to critically affect object recognition. Here we asked whether the feature of objects’ real-world size constructed an axis of object space with deep convolutional neural networks (DCNNs) based on three criteria of sensitivity, independence and necessity that are impractical to be examined altogether with traditional approaches. A principal component analysis on features extracted by the DCNNs showed that objects’ real-world size was encoded by an independent axis, and the removal of this axis significantly impaired DCNN’s performance in recognizing objects. With a mutually-inspired paradigm of computational modeling and biological observation, we found that the shape of objects, rather than retinal size, co-occurrence, task demands and texture features, was necessary to represent the real-world size of objects for DCNNs and humans. In short, our study provided the first evidence supporting the feature of objects’ real-world size as an axis of object space, and devised a novel paradigm for future exploring the structure of object space. Neurology Systems and Networking Real-world Size Object Space Object Recognition Deep Convolutional Neural Network fMRI Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Published Journal Publication published 27 Jul, 2022 Read the published version in Communications Biology → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1055467","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":63230491,"identity":"83c54191-574a-449a-944e-28fcfc68c079","order_by":0,"name":"Taicheng Huang","email":"","orcid":"https://orcid.org/0000-0002-7670-1124","institution":"Tsinghua University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Taicheng","middleName":"","lastName":"Huang","suffix":""},{"id":63230492,"identity":"abb55448-abfb-480f-9363-df05d38bf112","order_by":1,"name":"Yiying Song","email":"","orcid":"","institution":"Beijing Normal University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yiying","middleName":"","lastName":"Song","suffix":""},{"id":63230493,"identity":"7c97edb1-3aa2-44fd-addf-20d34c0b9a88","order_by":2,"name":"Jia Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIie2RP0vDQBTAXzm4LNGuJ4L9Ci8Iuvhh7pG9FApOtQSEG7MX9DtkknZLCTRLsGuLS4qrYlxEUcSXdumSs2Oh9+Px/sD78Q4OwOHYQ44FAGoADV5U5zoY36LIjcLhZ7sqm8KKCtfdDornY68c/HRx+ZZVq/FNF/O0Vb4a6Fw2PsxH1DPs41MoFRV5HwstgnsDwSSyKRIpYQXIzChJtTw9MqAxtSm/rCwzUa2Veel9/6uQYWUhQJEZcKOlsCuyhxSf06gIL1hJabRY3Z7cPaogaVDa7ewh+Pw4ozifPr9/mSHF83BavVxfdZqu1Idwa+D/hFbESTXuM6LcGoa2TYfD4ThQ/gDcVV5BUzAm9gAAAABJRU5ErkJggg==","orcid":"","institution":"Tsinghua University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2021-11-06 09:10:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1055467/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1055467/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s42003-022-03711-3","type":"published","date":"2022-07-27T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":24435977,"identity":"48ce1e54-402b-4c31-8ef8-8f1899c37396","added_by":"auto","created_at":"2022-07-28 07:09:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1823692,"visible":true,"origin":"","legend":"","description":"","filename":"SizeAxisManuscriptCommunicationBiology.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1055467/v1_covered.pdf"},{"id":15563628,"identity":"663e6961-2b10-4ee8-b450-d5054883231f","added_by":"auto","created_at":"2021-11-15 19:50:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1818660,"visible":true,"origin":"","legend":"","description":"","filename":"SizeAxisManuscriptCommunicationBiology.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1055467/v1_covered.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Real-world size of objects serves as an axis of object space","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1055467/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Real-world Size, Object Space, Object Recognition, Deep Convolutional Neural Network, fMRI","lastPublishedDoi":"10.21203/rs.3.rs-1055467/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1055467/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Our mind can represent various objects from the physical world metaphorically into an abstract and complex high-dimensional object space, with a finite number of orthogonal axes encoding critical object features. 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