A scoping review on behavioral, subjective, and physiological approaches towards human detection of natural vs synthetic stimuli: State of the art and quality assessment

Abstract

Recent advances in artificial intelligence, along with the increasing availability of generative models, have enabled the production of highly realistic synthetic content across multiple modalities. The increasing realism of these synthetic outputs raises important questions regarding the human ability to detect them, representing a new challenge for human perception at multiple levels. Addressing this requires an integrative approach focused not only on behavioral performance, but also subjective experience and physiological indicators. The present review synthesizes the empirical evidence on human detection of natural and synthetic stimuli across images, text, audio and video, particularly examining responses at behavioral, subjective and physiological levels, while also providing an examination of the overall quality of current research efforts in this field. A systematic search was conducted across five databases following the PRISMA-ScR guidelines, identifying a total of 97 eligible studies. Across the literature, most studies have focused on behavioral approaches (94.85%), consistently revealing that detection performance is often near chance level in particular for highly realistic visual stimuli. A strong dissociation between accuracy rates and subjective confidence is also frequently reported, with these evaluations being shaped by familiarity and contextual cues. Physiological evidence further suggests that natural and synthetic stimuli can produce distinguishable patterns of gaze and neural activity, even when behavioral accuracy does not reflect these differences. Overall, the literature shows high methodological heterogeneity and limitations in stimulus control and reporting practices. These findings highlight the need for integrative approaches to better understand human detection and inform strategies for improving judgements in digital environments.