While generative AI is now widespread and useful in society, there are potential risks of misuse, e.g., unconsciously influencing cognitive processes or decision-making. Although this causes a security problem in the cognitive domain, there has been no research about neural and computational mechanisms counteracting the impact of malicious generative AI in humans. We propose DecNefGAN, a novel framework that combines a generative adversarial system and a neural reinforcement model. More specifically, DecNefGAN bridges human and generative AI in a closed-loop system, with the AI creating stimuli that induce specific mental states, thus exerting external control over neural activity. The objective of the human is the opposite, to compete and reach an orthogonal mental state. This framework can contribute to elucidating how the human brain responds to and counteracts the potential influence of generative AI.

翻译：尽管生成式人工智能已在社会中广泛应用且具有实用性，但其存在被滥用的潜在风险，例如无意识地影响认知过程或决策制定。虽然这构成了认知领域的安全问题，但目前尚无研究探讨人类大脑应对恶意生成式AI影响的神经与计算机制。我们提出DecNefGAN这一创新框架，该框架融合了生成对抗系统与神经强化学习模型。具体而言，DecNefGAN在闭环系统中搭建了人类与生成式AI之间的桥梁：AI通过生成诱发特定心理状态的刺激，从而对神经活动施加外部控制。而人类的目标则相反，即通过竞争达到正交心理状态。该框架有助于阐明人脑如何响应并抵销生成式AI的潜在影响。