RMIT University, Melbourne, Australia.
Eur J Neurosci. 2023 Aug;58(3):2657-2661. doi: 10.1111/ejn.16052. Epub 2023 Jun 6.
The rise of deepfakes and AI-generated images has raised concerns regarding their potential misuse. However, this commentary highlights the valuable opportunities these technologies offer for neuroscience research. Deepfakes deliver accessible, realistic and customisable dynamic face stimuli, while generative adversarial networks (GANs) can generate and modify diverse and high-quality static content. These advancements can enhance the variability and ecological validity of research methods and enable the creation of previously unattainable stimuli. When AI-generated images are informed by brain responses, they provide unique insights into the structure and function of visual systems. The authors argue that experimental psychologists and cognitive neuroscientists stay informed about these emerging tools and embrace their potential to advance the field of visual neuroscience.
深度伪造和人工智能生成图像的兴起引发了人们对其潜在滥用的担忧。然而，本篇评论强调了这些技术为神经科学研究带来的宝贵机会。深度伪造可以提供易于使用、真实且可定制的动态面部刺激，而生成对抗网络 (GAN) 可以生成和修改多样化、高质量的静态内容。这些进展可以增强研究方法的可变性和生态有效性，并使以前无法实现的刺激成为可能。当人工智能生成的图像由大脑反应提供信息时，它们为视觉系统的结构和功能提供了独特的见解。作者认为，实验心理学家和认知神经科学家应该了解这些新兴工具，并接受它们在推进视觉神经科学领域的潜力。
