AI响应速度影响人机信任机制，快则盲从，慢则犹豫，需动态调整协同策略。
The Timing Dependencies of Trust: Speed, Accuracy, and cBCI Neuro-Decoupling in Human-AI Teams
- 根据AI响应速度分快（低精度）与慢（高精度），研究人机协同中的信任演化。
- 快速AI致人类准确率降至50.2%，慢速AI使人类犹豫但可恢复至100%准确率。
- 基于黎曼几何的自适应系统可识别不同信任状态并优化融合策略，适合高负载人机协作场景。
AI响应速度与准确性从根本上改变人机协作的失败模式。高速低精度AI易引发盲从，延迟高精度AI则导致认知冲突。本研究在虚拟现实无人机任务中，考察了快速/低精度（FLA-AI）与慢速/高精度（SA-AI）AI助手对协同脑机接口（cBCI）团队的影响。17名操作员在高负荷任务中完成持续搜索，其空间协方差通过2D自适应黎曼正则器（Adaptive Riemannian Oracle）建模。结果表明：快速AI引发即时盲从，人类准确率跌至50.2%，纯行为团队（N=8）无法超过74.1%；慢速AI引发延迟认知冲突，人类准确率下降至61.1%，但小规模团队（N=8）最终恢复至100.0%。黎曼正则器据此动态调节时间窗口——对快速反应限制<0.8秒，对延迟冲突放宽>1.2秒。通过混合融合整合真实信号，成功提升快速团队表现（N=8时+7.6%），加速小规模慢速团队恢复（N=4时+6.9%）。研究证明cBCI协同高度依赖信任的时间动态，为动态门控人机系统设计提供关键框架。
原文摘要 · Abstract (English)
The speed and accuracy of an artificial teammate fundamentally alter the failure states of Human-AI integration. While high-speed AI interventions risk inducing reflexive blind compliance, delayed interventions can induce ambiguous cognitive conflict. This study investigates how the fundamental characteristics of an in-task AI assistant, Fast/Less-Accurate (FLA-AI) versus Slow/Accurate (SA-AI) impact the synergy of Collaborative Brain-Computer Interface (cBCI) teams in a Virtual Reality drone task. Seventeen operators completed continuous search tasks under high cognitive workload while their spatial covariance was mapped using a 2D Adaptive Riemannian Oracle. The results mathematically demonstrate that AI timing dictates the mechanism of team failure. Fast AI induced instant, blind compliance; human accuracy under deception collapsed to 50.2%, and pure behavioural teams (N=8) failed to scale beyond 74.1%. In contrast, Slow AI induced delayed cognitive conflict; humans hesitated (61.1% accuracy), but N=8 behavioural teams eventually recovered to 100.0%. Crucially, the Riemannian Oracle mathematically adapted to these states: it heavily restricted temporal windows (< 0.8s) to intercept fast reflexive compliance, while widening windows (> 1.2s) to capture delayed cognitive conflict. Integrating these isolated veridical signals via Hybrid Fusion successfully rescued the Fast AI team (+7.6% at N=8) and significantly accelerated the recovery of smaller Slow AI teams (+6.9% at N=4). These findings prove that cBCI synergy is heavily contingent on the temporal dynamics of trust, providing a critical framework for designing dynamically gated Human-AI systems.
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