Individual Color Vision Prediction from Cone Ratios: A Computational Approach

Individual Color Vision Prediction from Cone Ratios: A Computational Approach | 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 Individual Color Vision Prediction from Cone Ratios: A Computational Approach Manoj v This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7726344/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Human color vision varies significantly between individuals due to differences in the biological structure of the eye. Specifically, the ratio of long-wavelength-sensitive (L) to medium-wavelength-sensitive (M) cone photoreceptorsthe specialized cells that detect colorranges from 1.1:1 to 16.5:1 across the population (Hofer et al., 2005) [1]. Despite two decades of research documenting these individual differences, no computational system has successfully predicted individual color perception from biological cone ratio measurements. Current computer vision approaches rely on population-average models that do not account for the individual variation observed in laboratory studies of human perceptual responses (psychophysical studies) (Fairchild, 2013; Hunt et al., 2005) [11, 12]. We developed a computational visual system capable of predicting individual color vision responses from anatomical cone ratio data. The system implements an individual scaling methodology where cone responses are weighted proportionally to each person’s L:M ratio relative to a universal baseline reference. We validated this approach through systematic testing against three independent experimental datasets: Stockman & Sharpe cone fundamentalsstandardized measurements of photoreceptor sensitivityachieving 6-12 nm spectral accuracy within individual variation tolerance (Stockman et al., 2000) [16], Neitz laboratory unique green measurements across 14 subjects (92.9% prediction success rate with 8.2 nm mean error) (Neitz et al., 2001) [3], and Greene et al. detection threshold experiments using identical stimulus conditions (Greene et al., 2012) [8]. The scaling formula predicts color discrimination variations, unique hue perceptions, and detection sensitivities across the biological range of human L:M ratios. This system enables personalized color vision research, clinical applications for individual assessment, and display optimization based on measured biological parameters. Biological sciences/Physiology/Neurophysiology Biological sciences/Computational biology and bioinformatics/Computational neuroscience ndividual color vision cone ratios L:M ratios computational modeling personalized vision psychophysics color perception Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7726344","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":522429578,"identity":"0c91dff8-5a2b-49f6-83c2-3140d2e31d25","order_by":0,"name":"Manoj v","email":"data:image/png;base64,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","orcid":"","institution":"Independent","correspondingAuthor":true,"prefix":"","firstName":"Manoj","middleName":"","lastName":"v","suffix":""}],"badges":[],"createdAt":"2025-09-27 06:35:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7726344/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7726344/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92529701,"identity":"af076c25-c403-4d07-9b01-ca073baa8002","added_by":"auto","created_at":"2025-09-30 16:34:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":238567,"visible":true,"origin":"","legend":"Article File","description":"","filename":"IndividualConePaper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7726344/v1_covered_8a524afe-c6cf-48f7-91b6-7fb8362f81e7.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Individual Color Vision Prediction from Cone Ratios: A Computational Approach","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ndividual color vision, cone ratios, L:M ratios, computational modeling, personalized vision, psychophysics, color perception","lastPublishedDoi":"10.21203/rs.3.rs-7726344/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7726344/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Human color vision varies significantly between individuals due to differences in the biological structure of the eye. Specifically,\r\nthe ratio of long-wavelength-sensitive (L) to medium-wavelength-sensitive (M) cone photoreceptorsthe specialized cells that\r\ndetect colorranges from 1.1:1 to 16.5:1 across the population (Hofer et al., 2005) [1]. Despite two decades of research documenting\r\nthese individual differences, no computational system has successfully predicted individual color perception from biological cone\r\nratio measurements. Current computer vision approaches rely on population-average models that do not account for the individual\r\nvariation observed in laboratory studies of human perceptual responses (psychophysical studies) (Fairchild, 2013; Hunt et al., 2005)\r\n[11, 12]. We developed a computational visual system capable of predicting individual color vision responses from anatomical cone\r\nratio data. The system implements an individual scaling methodology where cone responses are weighted proportionally to each\r\nperson’s L:M ratio relative to a universal baseline reference. We validated this approach through systematic testing against three\r\nindependent experimental datasets: Stockman \u0026 Sharpe cone fundamentalsstandardized measurements of photoreceptor sensitivityachieving\r\n6-12 nm spectral accuracy within individual variation tolerance (Stockman et al., 2000) [16], Neitz laboratory unique green\r\nmeasurements across 14 subjects (92.9% prediction success rate with 8.2 nm mean error) (Neitz et al., 2001) [3], and Greene et al.\r\ndetection threshold experiments using identical stimulus conditions (Greene et al., 2012) [8]. The scaling formula predicts color\r\ndiscrimination variations, unique hue perceptions, and detection sensitivities across the biological range of human L:M ratios. 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