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

2026-10-07

AI Summary

This study addresses the challenge that AI-generated faces are increasingly indistinguishable from real ones, making it difficult to disentangle the effects of image provenance and disclosure labels on cognitive judgments. Employing a crossover experimental design integrating event-related potentials (ERPs), eye tracking, and online validation, this work provides the first empirical evidence demonstrating that labels, rather than the images themselves, dominate early attentional allocation and subsequent reappraisal. Specifically, disclosure labels significantly elicit the N2 component associated with early attention and the P3 component linked to conflict reappraisal, while also altering visual exploration patterns. Furthermore, this research contributes a validated stimulus set and offers a neuroscience-based foundation for designing effective disclosure mechanisms for AI-generated content.

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.

Problem

• AI-generated faces
• source labels
• face evaluation
• disclosure design
• EEG

Innovation

• AI disclosure
• event-related potentials
• eye-tracking
• face evaluation
• source labeling