Implicit neural measures of trust in artificial intelligence

Trust in AI systems is critical for effective collaboration, yet traditional measures—such as self-reports and behavioral proxies—are limited in capturing its dynamic and latent nature. This study introduces the contralateral delay activity (CDA), a neural marker of visual working memory load, as a novel, objective index of trust. While the CDA has been widely used in change detection tasks to track memory load, we repurpose it here to measure how many working memory resources users offload to an AI partner. Participants performed a lateralized memory task under low and high working memory load, collaborating with an AI whose reliability was experimentally manipulated across three phases: trust formation, violation, and restoration. In dyad trials, where the AI was responsible for one hemifield, CDA amplitude served as an index of how much information participants chose to maintain themselves versus offload. As AI reliability increased, CDA amplitude rose, indicating greater trust and reliance. When reliability dropped, participants encoded more from both hemifields, and CDA amplitude declined. During trust restoration, CDA amplitude returned to pre-violation levels, indicating renewed reliance—though it never matched the high amplitude of solo trials, suggesting lingering mistrust. Behavioral measures (e.g., reliance, compliance, response time) tracked these dynamics but lacked the resolution and specificity of CDA. Together, these results establish CDA as a powerful neural index of dynamic trust. It captures trial-by-trial fluctuations in offloading behavior that reflect users’ evolving confidence in AI assistance, offering a continuous, covert, and cognitively grounded measure of trust in interactive settings.