Iterative decomposition of visuomotor, device and cognitive variance in large scale online cognitive test data | 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 Iterative decomposition of visuomotor, device and cognitive variance in large scale online cognitive test data Valentina Giunchiglia, Dragos Gruia, Annalaura Lerede, William Trender, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2972434/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 Online cognitive assessment technologies are gaining traction as scalable and cost-effective alternatives to traditional supervised testing. However, variability in peoples’ home devices, and their visual and motor abilities, confound the cognitive specificity of online task performance scores. To address these limitations, we develop IDoCT (Iterative Decomposition of Cognitive Tasks), a novel method for estimating cognitive abilities and trial-difficulty scales from task performance timecourses in a data-driven manner while accounting for device and visuomotor latencies, and speed-accuracy trade-offs. IDoCT can operate with any computerised task that manipulates cognitive difficulty across trials. Using data from 388,757 adults across 12 online cognitive tasks, we show that IDoCT successfully dissociates cognitive abilities from visuomotor response latencies. The resultant cognitive scores exhibit superior psychometric structure and associations with demographic factors while being insensitive to testing device. We propose that IDoCT can enhance the precision of online cognitive assessments for diverse clinical and research applications. Biological sciences/Neuroscience/Cognitive neuroscience/Intelligence Biological sciences/Neuroscience/Cognitive neuroscience Biological sciences/Neuroscience/Computational neuroscience Full Text Additional Declarations Yes there is potential Competing Interest. A.H. is owner and founder of Future Cognition Ltd. and H2 Cognitive Designs Ltd., which develop custom cognitive assessment software for other university-based research groups. P.J.H. is the owner and co-founder of H2 Cognitive Designs Ltd. The authors report no other conflicts of interest. Supplementary Files SupplementaryMaterial1.pdf 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. 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