Interpretable abstractions of artificial neural networks predict behavior and neural activity during human information gathering

Interpretable abstractions of artificial neural networks predict behavior and neural activity during human information gathering | 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 Interpretable abstractions of artificial neural networks predict behavior and neural activity during human information gathering Simone D'Ambrogio, Jan Grohn, Nima Khalighinejad, Marcelo Mattar, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6976160/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract It has been suggested that humans and other animals are driven by a fundamental desire to acquire information about opportunities available in their environments. Not only might such a desire explain pathological behaviors, but it may be needed to account for how everyday decisions are resolved. Here, we combine artificial neural networks (ANNs) with symbolic regression to extract an expressive yet interpretable model that specifies how human participants evaluate decision-relevant information during choice. This model accounts for behavior in our own data and in previous work, outperforming existing accounts of information sampling such as the Upper Confidence Bound heuristic. This modelling approach has broad potential for uncovering novel patterns in behavior and cognitive processes, while also specifying them in human-interpretable formats. We then used the value of information derived by our model, together with ultra-high field neuroimaging, to examine activity across a suite of subcortical neuromodulatory nuclei and two cortical regions that influence these nuclei. This established roles for midbrain dopaminergic nuclei, anterior cingulate cortex, and anterior insula in mediating the influence of value of information on behavior. Biological sciences/Neuroscience/Cognitive neuroscience/Decision Biological sciences/Neuroscience/Computational neuroscience/Network models Decision-making Artificial neural network Symbolic regression Computational modeling Information sampling Value of information Neuroimaging Full Text Additional Declarations There is NO Competing Interest. Supplementary Files supplementary.pdf Supplementary Infomrations Cite Share Download PDF Status: Under Review 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. 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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-6976160","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":486459567,"identity":"70f14d05-aaff-4d35-ac74-a5293b0af0c6","order_by":0,"name":"Simone 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