EEG and Eye-Tracking Evidence That AI Disclosure Shapes Face Evaluation

Abstract:AI-generated faces can be difficult to distinguish from real ones, leaving viewers to rely on source labels when judging an image. Yet prior work has made it difficult to separate the effects of what an image actually is from what viewers are told it is. We validated faces as AI-generated or human in an online study (N=169), then crossed actual source (AI, human) with label (none, Made with AI, Made by a human) in a lab study $N=30), recording event-related potentials (ERPs) and gaze. ERP responses were equivalent for AI-generated and real faces, but varied with the label: labels drew early attention (N2), while labels that conflicted with the face's actual source prompted re-evaluation of the face (P3). Affective processing and initial gaze orienting were unchanged, but labels altered visual exploration. We provide a validated stimulus set and evidence that attributed origin shapes face processing, with implications for disclosure design.

Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)

arXiv:2610.10182 [cs.HC]

(or arXiv:2610.10182v1 [cs.HC] for this version)

https://doi.org/10.48550/arXiv.2610.10182 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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