认知神经科学
系统神经科学
脑功能
生成语法
领域(数学)
对抗制
心理学
认知科学
计算机科学
神经科学
功能(生物学)
面子(社会学概念)
认知
数据科学
人工智能
社会学
生物
进化生物学
社会科学
纯数学
中枢神经系统
少突胶质细胞
髓鞘
数学
作者
Casey Becker,Robin Laycock
摘要
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.
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