How Cognitive Load Affects Dynamic Trust Calibration in Human–AI Collaboration: Evidence for Selective Pathway Effects

ObjectiveThis study examined whether cognitive load produces selective effects on different trust updating pathways in AI-assisted decision making.BackgroundAlthough cognitive load affects trust in automation, its influence on the mechanisms of trial-by-trial trust updating remains unclear.MethodsA dual-task paradigm embedded in a mining exploration task manipulated cognitive load while capturing dynamic trust calibration. Guided by a dual-pathway framework, we operationalized process-based (analytical evaluation of AI recommendation correctness) and outcome-based (heuristic reliance on task outcomes) trust updating pathways. Trust dynamics and behavioral reliance were examined using linear mixed-effects models.ResultsCognitive load shifted the relative influence of the two trust updating pathways. Process-based updating was attenuated under high cognitive load, indicating reduced sensitivity to AI recommendation correctness during trust updating. Outcome-based information gained greater influence under high load, amplifying outcome-driven bias regardless of recommendation correctness. Asymmetric trust updating was evident overall, although the influence of cognitive load on this asymmetry depended on task outcomes. Overall, high cognitive load elevated both subjective trust and behavioral reliance on AI.ConclusionCognitive load shapes trust calibration through mechanism-level reconfiguration rather than global impairment. By revealing how cognitive constraints rebalance dual trust pathways-weakening analytic evaluation while amplifying heuristic outcome reliance-this study advances theoretical understanding of dynamic trust in human-AI collaboration.ApplicationThe results provide practical guidance for the design of AI systems in high-stakes settings, highlighting the need to support analytic trust updating and mitigate over-reliance under cognitive strain.