The Oomplet Dataset Toolkit: A flexible and extensible system for large-scale, multi-category image generation

The Oomplet Dataset Toolkit: A flexible and extensible system for large-scale, multi-category image generation | 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 The Oomplet Dataset Toolkit: A flexible and extensible system for large-scale, multi-category image generation John P. Kasarda, Angela Zhang, Hua Tong, Yuan Tan, Ruizi Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4618904/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Understanding the dynamics of perceptual learning in humans, non-human animals, and artificial agents requires large stimulus sets with flexible features that can be used to discriminate across categorical groups. Here we introduce the Oomplet Dataset Toolkit (ODT), an open-source, publicly available toolbox for generating up to 9.1 million unique visual stimuli that are assembled across ten different feature dimensions. The resulting stimuli consist of cartoon-like humanoid characters -- ''Oomplets'' -- that are meant to be engaging, pleasant to look at, and can be appropriately used in research on a variety of populations, including children. Across several behavioral experiments, we show how eight of the ten possible dimensions that define an individual Ooomplet can be used by adults as effective classification boundaries for simple perceptual discrimination. The ODT thus provides a flexible and customizable way for generating very large and novel stimulus sets in order to study perceptual learning in both biological and artificial systems. Biological sciences/Psychology/Human behaviour Physical sciences/Mathematics and computing/Computer science Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 18 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 04 Oct, 2024 Reviews received at journal 11 Sep, 2024 Reviewers agreed at journal 11 Sep, 2024 Reviews received at journal 06 Aug, 2024 Reviewers agreed at journal 16 Jul, 2024 Reviewers agreed at journal 16 Jul, 2024 Reviewers invited by journal 16 Jul, 2024 Editor assigned by journal 16 Jul, 2024 Editor invited by journal 25 Jun, 2024 Submission checks completed at journal 24 Jun, 2024 First submitted to journal 21 Jun, 2024 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-4618904","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":325628017,"identity":"a58a5d0f-80a9-4caf-ba7e-7af09f693a2e","order_by":0,"name":"John P. 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