This project will develop a closed-loop, physiologically informed AI platform that uses neural, systemic physiological, and affective signals—such as EEG, arousal, and stress markers—to infer latent cognitive and emotional states during naturalistic decision-making tasks. These inferred (latent) states will be detected online through a closed-loop brain–computer interface and converted into rich reinforcement signals for training DRL agents. By dynamically learning from these signals, the AI agent will begin to model human intentions – an emerging form of machine “theory of mind” – and ultimately help inform upcoming human behaviour. The ultimate goal is to close the human–AI interaction loop: enabling the AI agent to feed predictions and adaptive support back to human users in real time, thereby augmenting decision-making, performance, and social coordination.

This project will develop a closed-loop, physiologically informed AI platform that uses neural, systemic physiological, and affective signals—such as EEG, arousal, and stress markers—to infer latent cognitive and emotional states during naturalistic decision-making tasks. These inferred (latent) states will be detected online through a closed-loop brain–computer interface and converted into rich reinforcement signals for training DRL agents. By dynamically learning from these signals, the AI agent will begin to model human intentions – an emerging form of machine “theory of mind” – and ultimately help inform upcoming human behaviour. The ultimate goal is to close the human–AI interaction loop: enabling the AI agent to feed predictions and adaptive support back to human users in real time, thereby augmenting decision-making, performance, and social coordination.