Role Framing and Trust in Medical AI: Evidence from a Scenario Experiment and EEG

Online medical AI has evolved from back-end analytical tools to patient-facing service interfaces, making calibrated trust an increasingly important design and governance problem. Drawing on role congruity theory, this study examined how expert and companion role framing is associated with trust across medical task contexts. Study 1 used a 2 (role: expert vs. companion) × 2 (task: illness consultation vs. health consultation) scenario experiment (N = 300). Expert framing increased perceived professionalism, whereas companion framing increased social presence; both perceptions were independently associated with overall trust when entered simultaneously. The indirect association through professionalism was conditioned by task context, but a direct bootstrap comparison did not show that the two moderated-mediation indices differed for overall trust. Measurement diagnostics indicated substantial overlap between perceived professionalism and cognitive trust and between social presence and affective trust, requiring cautious interpretation. Study 2 compared expert- and companion-framed statements using EEG in 39 paired observations. Expert framing was accompanied by higher frontal-midline theta ERS and a less negative baseline-corrected FCz-F6 theta ΔPLV, while condition-level behavior showed no reliable difference in trust rate, response time, or missing-response rate. These EEG findings are exploratory process-level correlates rather than neural validation of the conditional indirect model. Overall, role cues appear to shape nonexclusive competence-oriented and relationship-oriented evaluations. Role design should support calibrated reliance through capability boundaries, communication of uncertainty, risk-based warnings, and appropriate escalation, rather than simply maximizing trust.