Generative AI-based closed-loop fMRI system
This addresses a security problem in the cognitive domain for humans, but it is incremental as it builds on existing generative AI and neuroscience methods.
The paper tackles the problem of malicious generative AI influencing human cognitive processes by proposing DecNefGAN, a closed-loop fMRI system that combines generative adversarial networks and neural reinforcement to study how humans counteract such AI influence, resulting in a framework that elucidates brain responses without specific numerical results.
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.