3 papers
cs.LG2025
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.…
cs.HC2025
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…
cs.HC2025
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…