Generative AI-based closed-loop fMRI system
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 5 of its 22 references that are in the map. First posted on arXiv 30 Jan 2024.
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References · 5
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,588
- 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,564
- 2022Generative Adversarial Networkscited by 5,614
- 2015Generic decoding of seen and imagined objects using hierarchical visual featurescited by 119
- 2016Differential activation patterns in the same brain region led to opposite emotional statescited by 5
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