A mechanistic model of trust based on neural information processing

Fuente: arXiv
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Hauptverfasser: Allen, Scott E., Kizilcec, René F., Redish, A. David
Format: Preprint
Veröffentlicht: 2024
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author Allen, Scott E.
Kizilcec, René F.
Redish, A. David
author_facet Allen, Scott E.
Kizilcec, René F.
Redish, A. David
contents Trust is central to human social interactions, manifesting in actions that make one vulnerable to another. We argue that trust will thus depend 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 from multiple parallel systems that perform distinct, complementary information processing. Because each system learns via different mechanisms, 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. 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, and repair trust in a complex social world.
format Preprint
id arxiv_https___arxiv_org_abs_2401_08064
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle A mechanistic model of trust based on neural information processing
Allen, Scott E.
Kizilcec, René F.
Redish, A. David
General Economics
Economics
Human-Computer Interaction
Neurons and Cognition
Trust is central to human social interactions, manifesting in actions that make one vulnerable to another. We argue that trust will thus depend 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 from multiple parallel systems that perform distinct, complementary information processing. Because each system learns via different mechanisms, 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. 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, and repair trust in a complex social world.
title A mechanistic model of trust based on neural information processing
topic General Economics
Economics
Human-Computer Interaction
Neurons and Cognition
url https://arxiv.org/abs/2401.08064