Automation Disrupts, Explanations Restore: The Neural Signatures of Agency Loss and Recovery in Human-AI Interaction

Engineering notes

Key topics: autonomous driving, control. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Automation has been shown to weaken the sense of agency (SoA), the experience of controlling one's actions and their outcomes, by disrupting the predictive link between intention and effect. Explainable AI (XAI) has been proposed as a solution, yet the neurocognitive mechanisms through which explanations restore agency remain unclear. Across three EEG experiments using an autonomous-driving paradigm, we examined how automation and different forms of AI explanations modulate explicit agency judgments and early neural markers of agency-related predictive processing. In Experiment 1, automation reduced explicit feelings of control and was associated with reduced sensory attenuation, as reflected by increased P1-N1 amplitudes, decreased N1-P2 amplitudes. In Experiment 2, distal (goal-level) explanations partially restored agency and selectively modulated early auditory responses, decreasing P1-N1 and increasing N1-P2 amplitudes. In Experiment 3, combining distal and proximal (trajectory-level) explanations produced the strongest behavioural and neural restoration of agency, yielding a graded attenuation of P1-N1 and enhanced N1-P2 responses. Across all experiments, mismatch negativity (MMN) remained unaffected, indicating that pre-attentive deviance detection is preserved regardless of agency or explainability. Together, these results identify component-specific EEG markers that track fluctuations in the sense of agency and demonstrate that multi-level intention sharing by AI systems enhances both predictive engagement and explicit control experience. This work provides a neurocognitive foundation for designing explainable autonomous systems capable of maintaining user agency.

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