A mechanistic model of trust based on neural information processing

[2401.08064] A mechanistic model of trust based on neural information processing Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Economics > General Economics arXiv:2401.08064 (econ) [Submitted on 16 Jan 2024 ( v1 ), last revised 14 Aug 2026 (this version, v3)] Title: A mechanistic model of trust based on neural information processing Authors: Scott E. Allen , René F. Kizilcec , A. David Redish View a PDF of the paper titled A mechanistic model of trust based on neural information processing, by Scott E. Allen and 2 other authors View PDF Abstract: Trust is central to human social interactions, manifesting as a critical information processing step in taking actions that make one vulnerable to another. We argue that trust depends on the decision-making processes that arise in neural systems. Building on advances in the cognitive neuroscience of decision making, we propose a mechanistic model of trust arising differently in multiple parallel systems that perform distinct, complementary information processing. Because each system learns via different computational mechanisms, they will interact with the environment differently, and trust can be created (or destroyed) in multiple ways. This systems- level taxonomy of information representations provides a principled basis for differentiating forms of trust, linking them to specific learning processes, and generating testable predictions about their expression in behavior. Furthermore, because these different computational processes are implemented by different neural circuits, our theory makes testable predictions about the different neural circuits underlying different kinds of trust. By situating trust within a broader theory of neural decision systems, our account unifies diverse findings across psychology, neuroscience, and the social sciences, and offers a foundation for explaining how humans develop, maintain, lose, and repair trust in a complex social world. Subjects: General Economics (econ.GN) ; Human-Computer Interaction (cs.HC); Neurons and Cognition (q-bio.NC) Cite as: arXiv:2401.08064 [econ.GN] (or arXiv:2401.08064v3 [econ.GN] for this version) https://doi.org/10.48550/arXiv.2401.08064 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Rene Kizilcec [ view email ] [v1] Tue, 16 Jan 2024 02:47:03 UTC (841 KB) [v2] Fri, 19 Sep 2025 20:34:33 UTC (1,664 KB) [v3] Fri, 14 Aug 2026 22:47:13 UTC (1,690 KB) Full-text links: Access Paper: View a PDF of the paper titled A mechanistic model of trust based on neural information processing, by Scott E. Allen and 2 other authors View PDF view license Current browse context: econ.GN < prev | next > new | recent | 2024-01 Change to browse by: cs cs.HC econ q-bio q-bio.NC q-fin q-fin.EC References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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