From the 1 of 5 linked papers with an AI index.
5 papers
An offline approach to fNIRS-guided reinforcement learning for robot behavior
Julia Santaniello, Madelaine Brower, Benson Jiang +4
The paper investigates using offline functional near‑infrared spectroscopy (fNIRS) brain signals to augment reinforcement learning for robot behavior, showing that neural data can…
Towards Reinforcement Learning from Neural Feedback: Mapping fNIRS Signals to Agent Performance
Julia Santaniello, Matthew Russell, Benson Jiang +3
Reinforcement Learning from Human Feedback (RLHF) is a methodology that aligns agent behavior with human preferences by integrating user feedback into the agent's training process.…
Mapping Neural Signals to Agent Performance, A Step Towards Reinforcement Learning from Neural Feedback
Julia Santaniello, Matthew Russell, Benson Jiang +3
Implicit Human-in-the-Loop Reinforcement Learning (HITL-RL) is a methodology that integrates passive human feedback into autonomous agent training while minimizing human workload.…
Neural and Cognitive Impacts of AI: The Influence of Task Subjectivity on Human-LLM Collaboration
Matthew Russell, Aman Shah, Giles Blaney +3
AI-based interactive assistants are advancing human-augmenting technology, yet their effects on users' mental and physiological states remain under-explored. We address this gap by…
Neural Signatures Within and Between Chess Puzzle Solving and Standard Cognitive Tasks for Brain-Computer Interfaces: A Low-Cost Electroencephalography Study
Matthew Russell, Samuel Youkeles, William Xia +3
Consumer-grade electroencephalography (EEG) devices show promise for Brain-Computer Interface (BCI) applications, but their efficacy in detecting subtle cognitive states remains un…