The cognitive map has been taken as the standard model for how agents infer the most efficient route to a goal location. Alternatively, path integration – maintaining a homing vector during navigation – constitutes a primitive and presumably less‐flexible strategy than cognitive mapping because path integration relies primarily on vestibular stimuli and pace counting. The historical debate as to whether complex spatial navigation is ruled by associative learning or cognitive map mechanisms has been challenged by experimental difficulties in successfully neutralizing path integration. To our knowledge, there are only three studies that have succeeded in resolving this issue, all showing clear evidence of novel route taking, a behaviour outside the scope of traditional associative learning accounts. Nevertheless, there is no mechanistic explanation as to how animals perform novel route taking. We propose here a new model of spatial learning that combines path integration with higher‐order associative learning, and demonstrate how it can account for novel route taking without a cognitive map, thus resolving this long‐standing debate. We show how our higher‐order path integration (HOPI) model can explain spatial inferences, such as novel detours and shortcuts. Our analysis suggests that a phylogenetically ancient, vector‐based navigational strategy utilizing associative processes is powerful enough to support complex spatial inferences.

中文翻译：

---

没有认知图的空间推理：高阶路径整合的作用

认知地图已被用作代理如何推断到达目标位置的最有效路线的标准模型。或者，路径整合——在导航过程中保持一个归航向量——构成了一种原始的并且可能比认知制图灵活的策略，因为路径整合主要依赖于前庭刺激和计步。关于复杂空间导航是否由关联学习或认知地图机制统治的历史争论一直受到成功中和路径整合的实验困难的挑战。据我们所知，只有三项研究成功解决了这个问题，所有研究都显示出采用新路线的明确证据，这是传统联想学习帐户范围之外的行为。尽管如此，关于动物如何执行新的路线选择，没有机械解释。我们在这里提出了一种新的空间学习模型，将路径整合与高阶关联学习相结合，并展示了它如何在没有认知地图的情况下解释新的路线选择，从而解决这一长期存在的争论。我们展示了我们的高阶路径集成 (HOPI) 模型如何解释空间推理，例如新颖的绕道和捷径。我们的分析表明，一种利用关联过程的系统发育古老的、基于向量的导航策略足以支持复杂的空间推断。并演示它如何在没有认知地图的情况下解释新的路线选择，从而解决这个长期存在的争论。我们展示了我们的高阶路径集成 (HOPI) 模型如何解释空间推理，例如新颖的绕道和捷径。我们的分析表明，一种利用关联过程的系统发育古老的、基于向量的导航策略足以支持复杂的空间推断。并演示它如何在没有认知地图的情况下解释新的路线选择，从而解决这个长期存在的争论。我们展示了我们的高阶路径集成 (HOPI) 模型如何解释空间推理，例如新颖的绕道和捷径。我们的分析表明，一种利用关联过程的系统发育古老的、基于向量的导航策略足以支持复杂的空间推断。