Learning how: A ‘mindless’ procedure alone gives rise to contextual-cueing – a weakly supervised connectionist model of statistical context learning in visual search

Learning how: A ‘mindless’ procedure alone gives rise to contextual-cueing – a weakly supervised connectionist model of statistical context learning in visual search | 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 Research Article Learning how: A ‘mindless’ procedure alone gives rise to contextual-cueing – a weakly supervised connectionist model of statistical context learning in visual search Werner Seitz, Artyom Zinchenko, Hermann J. Müller, Thomas Geyer This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8797646/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 Because our environment is not random, it is beneficial to assimilate the statistics of sensory impressions and improve performance, such as visual search for a target object in a cluttered array of non-target objects (contextual cueing – CC – effect). Computational models of CC have so far focused on predicting the target location from a particular configuration of non-target items. This contrasts with recent findings according to which display repetitions train human participants’ general procedures for the search task. Here, we test the latter idea by employing a connectionist model of visual search that exclusively learns a search procedure without acquiring any individual display-layout information. We show that an instance of a “learning how” mechanism not only proposes a viable alternative account to existing “learning that” mechanisms, but also generates more plausible key behavioral metrics and exhibits a central bias as an emergent phenomenon of learning-induced plasticity. These findings have implications for models of visual search and artificial intelligence: Learning a procedure from leveraging a task’s structure alone can mimic the effects of top-down modulation of attention, while also reducing the need for supervision in learning, thereby making computational models that leverage procedural learning behaviorally more plausible and easier to train. Computational Neuroscience Artificial Intelligence and Machine Learning Cognitive Neuroscience Psychology Mathematical and Theoretical Biology Visual search selective attention statistical learning contextual cueing Hebbian Learning computational modeling Full Text Additional Declarations The authors declare no competing interests. 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-8797646","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":586388511,"identity":"4488d6fc-490e-4182-a353-4a5c89e63ccb","order_by":0,"name":"Werner Seitz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYFACxgYE+yOYzdh4gIENr5ZGuB7GmQ0MEiBDCGhBsoaZF6yFgQGvFvn25vaHPxgOy5nPbj722XaHTZ1u+2GgLWU2OLUYnDnY2CDBcNhY5s6x5Nm5Z9IkzM4kArWcS8OtRSKxscHw3+3EGRI5xsy5bYclzA4AtTC2HcbtsPkPGxsSGG7Xz5DI/8xsCdJy/iFIy3/cnrkBDLEDDLcTJCRymJkZQVpugG05gMcviY3AoP1vOEMizZixty1NctsNoC0J55JxO6z9+IOPPxjS5CUkkh8z/Gyz4Tc7n/7wwYcyO9wOww4SSNUwCkbBKBgFowAFAACAQ1xUtJH2IQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-8483-5109","institution":"Ludwig-Maximilians-Universität München","correspondingAuthor":true,"prefix":"","firstName":"Werner","middleName":"","lastName":"Seitz","suffix":""},{"id":586388512,"identity":"235287fa-9233-4b87-90ef-1a2d46c39c21","order_by":1,"name":"Artyom Zinchenko","email":"","orcid":"","institution":"Ludwig-Maximilians-Universität München","correspondingAuthor":false,"prefix":"","firstName":"Artyom","middleName":"","lastName":"Zinchenko","suffix":""},{"id":586388513,"identity":"6e82ce68-de3a-47fa-82f4-77ee472a615a","order_by":2,"name":"Hermann J. 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