Controllability arbitrates between distinct emotions with opposing effects on behavior | 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 Controllability arbitrates between distinct emotions with opposing effects on behavior Levi Solomyak, Michael Hallquist, Alexandre Dombrovski, Eran Eldar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7864842/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 Emotions consistently shape learning and decision-making, yet their study remains challenging because they are internal states that cannot be directly observed. Recent theory offers a way to overcome this challenge by mapping two classes of emotions onto distinct reinforcement-learning computations, with environmental controllability determining which computation dominates. In controllable settings, emotions guide actions, motivating greater investment of effort and other resources following disappointing outcomes to improve performance. In uncontrollable settings, emotions track reward availability, with disappointing outcomes suppressing reward-seeking behavior and good outcomes amplifying it. We tested this model in a treasure-hunt task (N=509) and found that controllability modulated emotional responses to prediction errors, which in turn determined changes in resource investment. Applying the framework to professional tennis matches (N=6,715) revealed parallel effects: performance changes reflected the same interaction between prediction errors and controllability. Thus, in the laboratory and the real world, controllability arbitrates between distinct emotional responses that shape adaptive and maladaptive behavior. Biological sciences/Psychology/Human behaviour Biological sciences/Neuroscience/Cognitive neuroscience/Decision Biological sciences/Neuroscience/Reward Full Text Additional Declarations There is NO Competing Interest. 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. 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