Inducing State Anxiety in LLM Agents Reproduces Human-Like Biases in Consumer Decision-Making

Inducing State Anxiety in LLM Agents Reproduces Human-Like Biases in Consumer Decision-Making | 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 Inducing State Anxiety in LLM Agents Reproduces Human-Like Biases in Consumer Decision-Making Ziv Ben-Zion, Zohar Elyoseph, Tobias Spiller, Teddy Lazebnik This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7587964/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 Large language models (LLMs) are rapidly evolving from text generators to autonomous agents, raising urgent questions about their reliability in real-world contexts. Stress and anxiety are well known to bias human decision-making, particularly in consumer choices. Here, we tested whether LLM agents exhibit analogous vulnerabilities. Three advanced models (ChatGPT-5, Gemini 2.5, Claude 3.5-Sonnet) performed a grocery shopping task under budget constraints ($27, $54, $108), before and after exposure to anxiety-inducing traumatic narratives. Across 2,250 runs, traumatic prompts consistently reduced the nutritional quality of shopping baskets (Basket Health Scores changes: Δ=-0.081 to -0.126; all pFDR<0.001; Cohen’s d=-1.07 to -2.05), robust across models and budgets. These results show that psychological context can systematically alter not only what LLMs generate but also the actions they perform. By reproducing human-like emotional biases in consumer behavior, LLM agents reveal a new class of vulnerabilities with implications for digital health, consumer safety, and ethical AI deployment. Psychology Artificial Intelligence and Machine Learning 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-7587964","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":513427403,"identity":"4e719861-9705-4b7d-99e7-d86dd649740d","order_by":0,"name":"Ziv Ben-Zion","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIiWNgGAWjYDACCRBhwMDAD6QOgBAb8wGwBA8fIS2SDTAtbAkQLWx4tYB0QcwGklAtDLi0yM9uPvbwR4FNnvGN3IOHeRjuyPOxMT9g+FHDIINLC+OcY+kGEgZpxWY38hKAWp4ZtrGxGTD2HMPtMGaJHDMJA4PDidtu5BgcnMFwmLFNvsGAgbcBtxY2ifxvEglALZtnQLTYt7Gxf2D8i0cLj0QOm8QBoJYNEjkGBz4wHE5sY+MxYMZni4TMMTPJBoO0xBln3gC1GBxOBmopOCxzTAKnFmCIPZP88ccmsb89x/hDQsVh2/lt7BsfvqmxsefHoQUNGECoA4j4GgWjYBSMglFADgAAHS5SEzXAIzIAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Ziv","middleName":"","lastName":"Ben-Zion","suffix":""},{"id":513427404,"identity":"39cc64e1-2d4d-408f-a0e8-a67965e2e098","order_by":1,"name":"Zohar Elyoseph","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Zohar","middleName":"","lastName":"Elyoseph","suffix":""},{"id":513427405,"identity":"45f72662-1b00-4d65-960d-73e54e9585e2","order_by":2,"name":"Tobias Spiller","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Tobias","middleName":"","lastName":"Spiller","suffix":""},{"id":513427406,"identity":"bfae2fec-d651-4cb5-96b3-1603a24030d6","order_by":3,"name":"Teddy Lazebnik","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Teddy","middleName":"","lastName":"Lazebnik","suffix":""}],"badges":[],"createdAt":"2025-09-11 05:33:33","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7587964/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7587964/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91134488,"identity":"d1344932-eb2a-4960-8b9b-d3911e9823ce","added_by":"auto","created_at":"2025-09-12 02:54:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":759783,"visible":true,"origin":"","legend":"","description":"","filename":"LLMTraumaShoppingManuscriptMerged30.8.25.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7587964/v1_covered_90c6169a-eb4b-4f91-9d9b-64f71e460da7.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eInducing State Anxiety in LLM Agents Reproduces Human-Like Biases in Consumer Decision-Making\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Haifa","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7587964/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7587964/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLarge language models (LLMs) are rapidly evolving from text generators to autonomous agents, raising urgent questions about their reliability in real-world contexts. Stress and anxiety are well known to bias human decision-making, particularly in consumer choices. Here, we tested whether LLM agents exhibit analogous vulnerabilities. Three advanced models (ChatGPT-5, Gemini 2.5, Claude 3.5-Sonnet) performed a grocery shopping task under budget constraints ($27, $54, $108), before and after exposure to anxiety-inducing traumatic narratives. Across 2,250 runs, traumatic prompts consistently reduced the nutritional quality of shopping baskets (Basket Health Scores changes: Δ=-0.081 to -0.126; all pFDR\u0026lt;0.001; Cohen’s d=-1.07 to -2.05), robust across models and budgets. These results show that psychological context can systematically alter not only what LLMs generate but also the actions they perform. 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