Sahaï Aïsha, Pacherie Elisabeth, Grynszpan Ouriel, Berberian Bruno
Département d'Etudes Cognitives, ENS, EHESS, Centre National de la Recherche Scientifique, Institut Jean-Nicod, PSL Research University, Paris, France.
ONERA, The French Aerospace Lab, Département Traitement de l'Information et Systèmes, Salon-de-Provence, France.
Front Neurorobot. 2017 Oct 13;11:52. doi: 10.3389/fnbot.2017.00052. eCollection 2017.
Nowadays, interactions with others do not only involve human peers but also automated systems. Many studies suggest that the motor predictive systems that are engaged during action execution are also involved during joint actions with peers and during other human generated action observation. Indeed, the comparator model hypothesis suggests that the comparison between a predicted state and an estimated real state enables motor control, and by a similar functioning, understanding and anticipating observed actions. Such a mechanism allows making predictions about an ongoing action, and is essential to action regulation, especially during joint actions with peers. Interestingly, the same comparison process has been shown to be involved in the construction of an individual's sense of agency, both for self-generated and observed other human generated actions. However, the implication of such predictive mechanisms during interactions with machines is not consensual, probably due to the high heterogeneousness of the automata used in the experimentations, from very simplistic devices to full humanoid robots. The discrepancies that are observed during human/machine interactions could arise from the absence of action/observation matching abilities when interacting with traditional low-level automata. Consistently, the difficulties to build a joint agency with this kind of machines could stem from the same problem. In this context, we aim to review the studies investigating predictive mechanisms during social interactions with humans and with automated artificial systems. We will start by presenting human data that show the involvement of predictions in action control and in the sense of agency during social interactions. Thereafter, we will confront this literature with data from the robotic field. Finally, we will address the upcoming issues in the field of robotics related to automated systems aimed at acting as collaborative agents.
如今，与他人的互动不仅涉及人类同伴，还包括自动化系统。许多研究表明，在动作执行过程中参与的运动预测系统，在与同伴的联合行动以及观察其他人产生的动作时也会被激活。事实上，比较器模型假说认为，预测状态与估计实际状态之间的比较能够实现运动控制，并且通过类似的功能，理解和预测观察到的动作。这种机制允许对正在进行的动作进行预测，并且对于动作调节至关重要，尤其是在与同伴的联合行动中。有趣的是，相同的比较过程已被证明参与了个体的能动感的构建，无论是对于自我产生的动作还是观察到的其他人产生的动作。然而，在与机器的交互过程中，这种预测机制的作用尚未达成共识，这可能是由于实验中使用的自动机具有高度的异质性，从非常简单的设备到全人形机器人。在人机交互过程中观察到的差异可能源于与传统低级自动机交互时缺乏动作/观察匹配能力。一致地，与这类机器建立联合能动性的困难可能源于同一个问题。在这种背景下，我们旨在回顾研究社会互动中与人类和自动化人工系统的预测机制的研究。我们将首先展示人类数据，这些数据表明预测在社会互动中的动作控制和能动感中的作用。此后，我们将把这些文献与机器人领域的数据进行对比。最后，我们将探讨机器人领域中与旨在充当协作代理的自动化系统相关的未来问题。
