标题： 使用元强化学习进行视觉运动映射的抽象学习 显示英文标题

标题： Learning to Abstract Visuomotor Mappings using Meta-Reinforcement Learning

摘要： 我们研究了人类获取多个视觉运动映射以生成新技能的能力。 使用网格导航范式，我们测试了作为不同“网格世界”的情境线索是否允许参与者更高效地学习两种不同的按键映射。 我们的结果表明，当提供情境信息时，任务表现显著更好。 对于在执行任务时是否接收情境信息的元强化学习代理来说，这一结论同样成立。 我们评估了它们预测人类任务表现的准确性，并分析了它们的内部表示。 结果表明，当使用不同的视觉运动映射时，情境线索允许在空间和时间上形成独立的表示，而缺乏这些线索则倾向于共享一个表示。 虽然这两种策略都可以允许学习多个视觉运动映射，但我们证明情境线索在可以学习的映射数量方面提供了计算优势。 显示英文摘要

摘要： We investigated the human capacity to acquire multiple visuomotor mappings for de novo skills. Using a grid navigation paradigm, we tested whether contextual cues implemented as different "grid worlds", allow participants to learn two distinct key-mappings more efficiently. Our results indicate that when contextual information is provided, task performance is significantly better. The same held true for meta-reinforcement learning agents that differed in whether or not they receive contextual information when performing the task. We evaluated their accuracy in predicting human performance in the task and analyzed their internal representations. The results indicate that contextual cues allow the formation of separate representations in space and time when using different visuomotor mappings, whereas the absence of them favors sharing one representation. While both strategies can allow learning of multiple visuomotor mappings, we showed contextual cues provide a computational advantage in terms of how many mappings can be learned.