Generative AI-based closed-loop fMRI system
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909087523930112 |
|---|---|
| author | Kasahara, Mikihiro Oka, Taiki Taschereau-Dumouchel, Vincent Kawato, Mitsuo Takakura, Hiroki Cortese, Aurelio |
| author_facet | Kasahara, Mikihiro Oka, Taiki Taschereau-Dumouchel, Vincent Kawato, Mitsuo Takakura, Hiroki Cortese, Aurelio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_16742 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generative AI-based closed-loop fMRI system Kasahara, Mikihiro Oka, Taiki Taschereau-Dumouchel, Vincent Kawato, Mitsuo Takakura, Hiroki Cortese, Aurelio Human-Computer Interaction Artificial Intelligence Cryptography and Security Machine Learning 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. |
| title | Generative AI-based closed-loop fMRI system |
| topic | Human-Computer Interaction Artificial Intelligence Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2401.16742 |