Much of our understanding of navigation comes from the study of individual species, often with specific tasks tailored to those species. Here, we provide a novel experimental and analytic framework, integrating across humans, rats and simulated reinforcement learning (RL) agents to interrogate the dynamics of behaviour during spatial navigation. We developed a novel open-field navigation task (ʻTartarus Maze’) requiring dynamic adaptation (shortcuts and detours) to frequently changing obstructions in the path to a hidden goal. Humans and rats were remarkably similar in their trajectories. Both species showed the greatest similarity to RL agents utilising a ʻsuccessor representation’, which creates a predictive map. Humans also displayed trajectory features similar to model-based RL agents, which implemented an optimal tree-search planning procedure. Our results help refine models seeking to explain mammalian navigation in dynamic environments, and highlight the utility of modelling the behaviour of different species to uncover the shared mechanisms that support behaviour.

中文翻译：

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用于空间导航的大鼠和人类预测地图

我们对导航的大部分理解来自对单个物种的研究，通常是针对这些物种量身定制的特定任务。在这里，我们提供了一个新的实验和分析框架，整合了人类、大鼠和模拟强化学习 (RL) 代理，以询问空间导航期间的行为动态。我们开发了一种新颖的开放领域导航任务（“Tartarus 迷宫”），需要动态适应（捷径和绕道）以适应通往隐藏目标的路径中频繁变化的障碍物。人类和老鼠的轨迹非常相似。这两个物种都表现出与使用“后继表示”的 RL 代理的最大相似性，后者创建了一个预测图。人类还表现出类似于基于模型的 RL 代理的轨迹特征，它实现了一个最优的树搜索规划程序。我们的结果有助于改进试图解释哺乳动物在动态环境中导航的模型，并强调对不同物种的行为进行建模以揭示支持行为的共享机制的效用。