🤖 AI Summary
This study investigates the cognitive mechanisms underlying differential trust in AI versus human information providers across factual and social contexts. Using a Bayesian hierarchical sequential sampling model (HSSM), we analyzed trust decisions from participants across 30 distinct scenarios. Results indicate that trust preferences are primarily driven by the evidence accumulation rate (drift rate), rather than prior biases or decision caution. A key contribution is the identification of domain-specific vigilance mechanisms: in factual contexts, negative drift rates accelerate erosion of trust in AI, whereas in social contexts, positive drift rates strengthen trust in humans; critically, drift rates correlate significantly with real-time confidence judgments. These findings establish a novel, dynamic evidence-accumulation framework for understanding the cognitive roots of AI trust fragility, moving beyond static attributional accounts to reveal how context-dependent accumulation dynamics shape trust formation in real time.
📝 Abstract
The integration of artificial intelligence into everyday decision-making has reshaped patterns of selective trust, yet the cognitive mechanisms behind context-dependent preferences for AI versus human informants remain unclear. We applied a Bayesian Hierarchical Sequential Sampling Model (HSSM) to analyze how 102 Colombian university students made trust decisions across 30 epistemic (factual) and social (interpersonal) scenarios.
Results show that context-dependent trust is primarily driven by differences in drift rate (v), the rate of evidence accumulation, rather than initial bias (z) or response caution (a). Epistemic scenarios produced strong negative drift rates (mean v = -1.26), indicating rapid evidence accumulation favoring AI, whereas social scenarios yielded positive drift rates (mean v = 0.70) favoring humans. Starting points were near neutral (z = 0.52), indicating minimal prior bias.
Drift rate showed a strong within-subject association with signed confidence (Fisher-z-averaged r = 0.736; 95 percent bootstrap CI 0.699 to 0.766; 97.8 percent of individual correlations positive, N = 93), suggesting that model-derived evidence accumulation closely mirrors participants' moment-to-moment confidence. These dynamics may help explain the fragility of AI trust: in epistemic domains, rapid but low-vigilance evidence processing may promote uncalibrated reliance on AI that collapses quickly after errors.
Interpreted through epistemic vigilance theory, the results indicate that domain-specific vigilance mechanisms modulate evidence accumulation. The findings inform AI governance by highlighting the need for transparency features that sustain vigilance without sacrificing efficiency, offering a mechanistic account of selective trust in human-AI collaboration.