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arXiv Machine Learning By Christopher Baker, Stephen Hinton, Akashdeep Nijjar, Riccardo Poli, Caterina Cinel, Tom Reed, Stephen Fairclough

The Timing Dependencies of Trust: Speed, Accuracy, and cBCI Neuro-Decoupling in Human-AI Teams

Read the original on arXiv Machine Learning →

The study examines how the speed and accuracy of an AI teammate—Fast/Less-Accurate (FLA-AI) versus Slow/Accurate (SA-AI)—affect performance in a collaborative Brain‑Computer Interface (cBCI) team during a virtual reality drone search task. Fast AI leads to instant, blind compliance and a sharp drop in human accuracy, while Slow AI induces delayed cognitive conflict that ultimately allows teams to recover and achieve perfect accuracy. A 2D Adaptive Riemannian Oracle and Hybrid Fusion techniques were used to adaptively capture and integrate these timing-dependent signals, improving team performance in both scenarios.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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