Universität Göttingen, Department for Computational Neuroscience at the Bernstein Center Göttingen, Inst. of Physics 3 and Leibniz Science Campus for Primate Cognition, Göttingen, Germany.
Vytautas Magnus University, Faculty of Informatics, Kaunas, Lithuania.
Sci Rep. 2020 Mar 4;10(1):3999. doi: 10.1038/s41598-020-60923-5.
Efficient action prediction is of central importance for the fluent workflow between humans and equally so for human-robot interaction. To achieve prediction, actions can be algorithmically encoded by a series of events, where every event corresponds to a change in a (static or dynamic) relation between some of the objects in the scene. These structures are similar to a context-free grammar and, importantly, within this framework the actual objects are irrelevant for prediction, only their relational changes matter. Manipulation actions and others can be uniquely encoded this way. Using a virtual reality setup and testing several different manipulation actions, here we show that humans predict actions in an event-based manner following the sequence of relational changes. Testing this with chained actions, we measure the percentage predictive temporal gain for humans and compare it to action-chains performed by robots showing that the gain is approximately equal. Event-based and, thus, object independent action recognition and prediction may be important for cognitively deducing properties of unknown objects seen in action, helping to address bootstrapping of object knowledge especially in infants.
动作预测对于人类之间的流畅工作流程以及人机交互都至关重要。为了实现预测，可以通过一系列事件对动作进行算法编码，其中每个事件对应于场景中一些对象之间的（静态或动态）关系的变化。这些结构类似于上下文无关语法，重要的是，在这个框架中，实际对象对于预测是无关紧要的，只有它们的关系变化才重要。操纵动作等可以以这种方式唯一地进行编码。使用虚拟现实设置并测试了几种不同的操纵动作，我们在这里表明人类以基于事件的方式根据关系变化的顺序进行动作预测。通过连锁动作进行测试，我们测量了人类的预测时间增益百分比，并将其与机器人执行的动作链进行了比较，结果表明增益大致相等。基于事件的，因此与对象无关的动作识别和预测对于认知推断在动作中看到的未知对象的属性可能很重要，有助于解决对象知识的自举问题，特别是在婴儿中。
