Where Do We Look? Visual Attention Competition Between Hyper-Salient Objects and AI-Generated Virtual Endorsers in Generative Artificial Intelligence Advertising

Generative artificial intelligence (AI) advertising, defined as advertising content created or substantially assisted by generative AI systems (AIGC), has introduced new visual features that combine algorithmically generated objects with human-like representations. Hyper-salient objects, defined by exaggerated perceptual prominence and semantic incongruity, and AI-generated virtual endorsers, represented by human-like AI-created figures, have become increasingly common in such advertisements. However, little is known about how these distinct visual cues compete for consumers’ limited attentional resources when presented together. Drawing on visual attention and scene perception perspectives, this study investigated how hyper-salient objects and AI-generated virtual endorsers influence visual attention allocation in AI-generated advertising. A 2 (hyper-salient object: present vs. absent) × 2 (AI endorser: present vs. absent) between-subjects eye-tracking experiment was conducted with 160 participants in a controlled laboratory setting using static AI-generated advertisements. Eye movements were recorded, and memory was assessed using signal detection measures (d′) from a recognition task involving target and distractor advertisements. Advertising attitude and product trust were measured using established scales. Results from linear and generalized linear mixed-effects models showed that hyper-salient objects increased early fixation probability and attracted a larger dwell time proportion, demonstrating strong attentional capture. AI-generated endorsers also attracted attention when presented alone, but their proportional attention share decreased when hyper-salient objects were simultaneously present. The effects of these visual elements on memory and product trust were conditional on their co-occurrence rather than uniform across conditions. Within the specific set of experimentally constructed stimuli and four sampled product categories, this research contributes to AIGC advertising literature by developing a visual competition perspective on how multiple AI-generated visual cues interact under limited attentional capacity, offering implications for designing effective generative AI advertisements.