Eroding the Truth-Default: A Causal Analysis of Human Susceptibility to Foundation Model Hallucinations and Disinformation in the Wild

• URL: http://arxiv.org/abs/2601.22871v1
• Date: Fri, 30 Jan 2026 11:49:58 GMT
• Title: Eroding the Truth-Default: A Causal Analysis of Human Susceptibility to Foundation Model Hallucinations and Disinformation in the Wild
• Authors: Alexander Loth, Martin Kappes, Marc-Oliver Pahl,
• Abstract summary: "Fake news familiarity" emerges as a candidate mediator, suggesting that exposure may function as adversarial training for human discriminators.<n>These findings suggest that "pre-bunking" interventions should target cognitive source monitoring rather than demographic segmentation.
• Score: 47.03825808787752
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As foundation models (FMs) approach human-level fluency, distinguishing synthetic from organic content has become a key challenge for Trustworthy Web Intelligence. This paper presents JudgeGPT and RogueGPT, a dual-axis framework that decouples "authenticity" from "attribution" to investigate the mechanisms of human susceptibility. Analyzing 918 evaluations across five FMs (including GPT-4 and Llama-2), we employ Structural Causal Models (SCMs) as a principal framework for formulating testable causal hypotheses about detection accuracy. Contrary to partisan narratives, we find that political orientation shows a negligible association with detection performance ($r=-0.10$). Instead, "fake news familiarity" emerges as a candidate mediator ($r=0.35$), suggesting that exposure may function as adversarial training for human discriminators. We identify a "fluency trap" where GPT-4 outputs (HumanMachineScore: 0.20) bypass Source Monitoring mechanisms, rendering them indistinguishable from human text. These findings suggest that "pre-bunking" interventions should target cognitive source monitoring rather than demographic segmentation to ensure trustworthy information ecosystems.

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