In most everyday life situations, the brain needs to engage not only in making decisions but also in anticipating and predicting the behavior of others. In such contexts, gaze can be highly informative about others’ intentions, goals, and upcoming decisions. Here, we investigated whether a humanoid robot’s gaze (mutual or averted) influences the way people strategically reason in a social decision-making context. Specifically, participants played a strategic game with the robot iCub while we measured their behavior and neural activity by means of electroencephalography (EEG). Participants were slower to respond when iCub established mutual gaze before their decision, relative to averted gaze. This was associated with a higher decision threshold in the drift diffusion model and accompanied by more synchronized EEG alpha activity. In addition, we found that participants reasoned about the robot’s actions in both conditions. However, those who mostly experienced the averted gaze were more likely to adopt a self-oriented strategy, and their neural activity showed higher sensitivity to outcomes. Together, these findings suggest that robot gaze acts as a strong social signal for humans, modulating response times, decision threshold, neural synchronization, as well as choice strategies and sensitivity to outcomes. This has strong implications for all contexts involving human-robot interaction, from robotics to clinical applications.

中文翻译：

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与机器人相互凝视会影响人类神经活动并延迟决策过程

在大多数日常生活情况下，大脑不仅需要参与决策，还需要预测和预测他人的行为。在这种情况下，凝视可以为他人的意图、目标和即将做出的决定提供大量信息。在这里，我们调查了人形机器人的凝视（相互或避开）是否会影响人们在社会决策环境中战略性推理的方式。具体来说，参与者与机器人 iCub 进行了一场战略游戏，而我们通过脑电图 (EEG) 测量了他们的行为和神经活动。当 iCub 在做出决定之前建立相互注视时，参与者的反应较慢，相对于回避注视。这与漂移扩散模型中更高的决策阈值相关，并伴随着更同步的 EEG α 活动。此外，我们发现参与者在这两种情况下都对机器人的行为进行了推理。然而，那些大部分经历过回避凝视的人更有可能采取自我导向的策略，他们的神经活动对结果表现出更高的敏感性。总之，这些研究结果表明，机器人凝视对人类来说是一种强烈的社会信号，可以调节响应时间、决策阈值、神经同步以及选择策略和对结果的敏感性。这对于从机器人技术到临床应用的所有涉及人机交互的环境都具有重要意义。这些发现表明，机器人凝视对人类来说是一种强烈的社交信号，可以调节响应时间、决策阈值、神经同步以及选择策略和对结果的敏感性。这对于从机器人技术到临床应用的所有涉及人机交互的环境都具有重要意义。这些发现表明，机器人凝视对人类来说是一种强烈的社交信号，可以调节响应时间、决策阈值、神经同步以及选择策略和对结果的敏感性。这对于从机器人技术到临床应用的所有涉及人机交互的环境都具有重要意义。