Model-based comparison of latency estimation methods for the pupillary light reflex

Model-based comparison of latency estimation methods for the pupillary light reflex | 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 Model-based comparison of latency estimation methods for the pupillary light reflex Marcel Schepelmann, Hans Georg Krojanski This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9209962/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Accurate estimation of pupillary light reflex (PLR) latency is important in clinical and research settings, yet reported results are often difficult to compare due to differences in hardware, sampling rates, and analysis methods. This study introduces a systematic and reproducible benchmark for evaluating PLR latency estimation algorithms using synthetic data with known ground-truth latency. Synthetic pupillograms were generated using an established dynamic model of the PLR, extended to handle short light impulses. Five commonly used latency estimation methods were evaluated under these conditions, with 1000 traces simulated per configuration. Performance was assessed using the mean absolute error (MAE) between estimated and ground-truth latency. Across most conditions, a method developed by Bergamin and Kardon, combining filtering, interpolation, and analysis of the first and second derivatives achieved the lowest MAE, although its performance deteriorated under high noise and low-intensity stimuli. In contrast, a simple piecewise linear fit showed consistent, moderate performance across configurations. Threshold-based detection performed well for strong stimuli but degraded for weak responses, while derivative-based methods were sensitive to noise and exponential curve-fitting performed inconsistently across stimulus conditions. Physical sciences/Engineering Physical sciences/Optics and photonics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 11 May, 2026 Editor invited by journal 30 Mar, 2026 Editor assigned by journal 25 Mar, 2026 Submission checks completed at journal 25 Mar, 2026 First submitted to journal 24 Mar, 2026 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. 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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-9209962","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":638848406,"identity":"e0663fa8-76d1-420f-b551-97363d616860","order_by":0,"name":"Marcel Schepelmann","email":"data:image/png;base64,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","orcid":"","institution":"Leibniz University Hannover","correspondingAuthor":true,"prefix":"","firstName":"Marcel","middleName":"","lastName":"Schepelmann","suffix":""},{"id":638848407,"identity":"e335d0cb-89eb-4daf-98ba-64d63a083fc1","order_by":1,"name":"Hans Georg Krojanski","email":"","orcid":"","institution":"Leibniz University Hannover","correspondingAuthor":false,"prefix":"","firstName":"Hans","middleName":"Georg","lastName":"Krojanski","suffix":""}],"badges":[],"createdAt":"2026-03-24 09:41:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9209962/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9209962/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109205455,"identity":"e25a08c5-2409-43e9-8dc0-4a7fe45c78eb","added_by":"auto","created_at":"2026-05-13 15:04:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":591173,"visible":true,"origin":"","legend":"","description":"","filename":"zydgpfxftdykchbrgpchwqpzbrgwcgqt.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9209962/v1_covered_12cee797-f90d-4fd1-b4a5-e8ce1ed0d66f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Model-based comparison of latency estimation methods for the pupillary light reflex","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9209962/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9209962/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Accurate estimation of pupillary light reflex (PLR) latency is important in clinical and research settings, yet reported results are often difficult to compare due to differences in hardware, sampling rates, and analysis methods. 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