Evolved living beings can anticipate the consequences of their actions in complex multilevel dynamic situations. This ability relies on abstracting the meaning of an action. The underlying brain mechanisms of such semantic processing of information are poorly understood. Here we show how our novel concept, known as time compaction, provides a natural way of representing semantic knowledge of actions in time-changing situations. As a testbed, we model a fencing scenario with a subject deciding between attack and defense strategies. The semantic content of each action in terms of lethality, versatility, and imminence is then structured as a spatial (static) map representing a particular fencing (dynamic) situation. The model allows deploying a variety of cognitive strategies in a fast and reliable way. We validate the approach in virtual reality and by using a real humanoid robot.

中文翻译：

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动态情况下战略交互的语义知识表示。


进化的生物可以在复杂的多层次动态情况下预测其行为的后果。这种能力依赖于抽象动作的含义。人们对这种信息语义处理的潜在大脑机制知之甚少。在这里，我们展示了我们的新颖概念（称为时间压缩）如何提供一种自然的方式来表示时变情况下动作的语义知识。作为测试平台，我们模拟了一个击剑场景，让受试者在攻击和防御策略之间做出决定。然后，每个动作在杀伤力、多功能性和紧迫性方面的语义内容被构造为代表特定击剑（动态）情况的空间（静态）地图。该模型允许以快速可靠的方式部署各种认知策略。我们在虚拟现实中并使用真实的人形机器人验证了该方法。