用脑电和生理数据揭示音乐推荐系统中信任的神经机制
Inferring trust in recommendation systems from brain, behavioural, and physiological data
- 结合脑电、瞳孔变化等多模态数据研究用户对推荐系统的信任
- 系统准确率直接影响信任度，并调节推荐线索对偏好的影响
- 为可信赖AI开发提供神经层面的可信度评估新路径
随着人们越来越依赖人工智能进行信息筛选和决策，对自动化系统合理赋予权信任变得日益重要。然而，当前对自动化信任的测量仍主要依赖主观且干扰用户的自我报告。本文以音乐推荐为范例，研究了信任在自动化系统中的神经与认知机制。结果表明，系统准确率直接关联用户信任，并调节推荐线索对音乐偏好的影响。通过强化学习模型建模用户奖励编码过程，进一步发现系统准确率、预期回报和预测误差均与脑电振荡活动及瞳孔直径变化相关。研究提供了信任校准的神经基础，并凸显多模态方法在构建可信AI系统中的潜力。
原文摘要 · Abstract (English)
As people nowadays increasingly rely on artificial intelligence (AI) to curate information and make decisions, assigning the appropriate amount of trust in automated intelligent systems has become ever more important. However, current measurements of trust in automation still largely rely on self-reports that are subjective and disruptive to the user. Here, we take music recommendation as a model to investigate the neural and cognitive processes underlying trust in automation. We observed that system accuracy was directly related to users' trust and modulated the influence of recommendation cues on music preference. Modelling users' reward encoding process with a reinforcement learning model further revealed that system accuracy, expected reward, and prediction error were related to oscillatory neural activity recorded via EEG and changes in pupil diameter. Our results provide a neurally grounded account of calibrating trust in automation and highlight the promises of a multimodal approach towards developing trustable AI systems.
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