Modeling Response Time Variation in Addition Tasks: An ACT-R Extension with EVC-Guided Pause States

Modeling Response Time Variation in Addition Tasks: An ACT-R Extension with EVC-Guided Pause States | 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 Modeling Response Time Variation in Addition Tasks: An ACT-R Extension with EVC-Guided Pause States Keisuke Takeuchi¹, Kimi Ueda¹, Hirotake Ishii¹, Hiroshi Shimoda¹ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7804364/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 Reproducing variability in human response times is a central challenge in cognitive modeling. While conventional ACT-R models can replicate the general shape of response time (RT) distributions, they often fail to capture trial-by-trial fluctuations in cognitive processing time. In this study, we propose an extended ACT-R model that introduces a novel 'pause state,' a transient suspension of cognitive processing hypothesized to facilitate fatigue recovery. The initiation of this pause state is governed by the Expected Value of Control (EVC) theory, which selects the action with the highest expected utility based on trade-offs between cognitive rewards and costs. To validate the model, we designed a new simple addition task that enables fine-grained measurement of RT variability. The task allows for the decomposition of response times into two phases using gaze-tracking data: one corresponding to cognitive processing and the other to retrieval and motor response. Model parameters were optimized using a genetic algorithm, and model performance was evaluated by comparing simulated and empirical RT distributions. Results demonstrate that the proposed model accounts for trial-level RT variability, particularly in cognitive processing time, more accurately than a traditional ACT-R model. These findings highlight the importance of EVC-guided pause states in modeling subtle fluctuations in cognitive activity and contribute to a deeper understanding of the mechanisms underlying RT variability. cognitive modeling response time variability cognitive architecture cognitive task eye movement. Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementalMaterial.docx 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-7804364","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":538816361,"identity":"42d297df-8fc1-4454-b8ce-2444c0eeb9d4","order_by":0,"name":"Keisuke 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