Cognitive maps for goal-directed decision making

摘要 认知地图指的是空间关系和非空间关系的内部表征 外部世界中的实体(人或物)或事件。已经有广泛的 最近的发现带来的兴奋，即使是连续的非空间任务维度 可以被组织起来，作为一张认知地图进行导航。这些研究表明，神经 物理空间中揭示的表示和计算可能只是一个实例 组织和“导航”任何与行为相关的连续任务的一般机制 维度(如空间、时间、声音频率、公制长度)。这种洞察力引发了耐人寻味的 在空间导航过程中揭示的成熟的编码原理也可能 用于理解在抽象和离散任务中的灵活决策 当他们基于认知地图时，在现实世界中很常见，比如让谁 与之合作或在哪里用餐。 环境或任务的认知地图非常强大，因为它使 从有限的经验中做出推论，这些经验可以极大地加速新的学习 甚至指导以前从未面对过的新决定。此外，类似的任务共享一个 整体结构可以彼此直接相关，从而促进快速概括 从一个任务或实体转移到另一个任务或实体。尽管对灵活的认知有着广泛的重要性， 我们对认知地图如何使这些新奇的推论和 泛化。更好地了解所涉及的机制也具有重要的临床意义 这意味着什么。事实上，不正常的推理、认知灵活性和泛化被认为 几种精神疾病的核心功能障碍，从精神分裂症到强迫症 强迫症表现为某些情绪障碍。因此，开发一种 人类体内这些组成过程的机械模型有可能告诉我们 对这些疾病的生物标志物和治疗目标进行原则性调查。 这项提议的目标是开发一种新的神经模型，以了解认知地图是如何 抽象和离散的任务在神经元上被表示，并被用来指导在 在人脑中做决定。我们开发了一种新的实验范式， 诱导人们形成抽象和离散的认知地图(例如社交网络)和 在决策过程中进行新颖的推理。为了开发我们的模型，我们将结合 这一范例中的学习和推理的计算模型与几何模型 源于空间导航的神经编码以及“表象”和计算 允许进行推断的功能磁共振成像分析方法 关于在不同的大脑结构中表示的信息和执行的计算， 分别进行了分析。从这项研究中获得的洞察力将带来实质性的理论进步 在目标导向决策的模型中，认知灵活性和记忆力具有暗示意义 对于典型的和非典型的个人。

Abstract Cognitive maps refer to internal representations of spatial and non-spatial relationships between entities (people or things) or events in the external world. There has been widespread excitement generated by recent discoveries that even continuous non-spatial task dimensions may be organized and ‘navigated’ as a cognitive map. These studies suggest the neural representations and computations revealed in physical space may be only one instance of a general mechanism for organizing and “navigating” any behaviorally-relevant continuous task dimensions (e.g. space, time, sound frequency, metric length). This insight raises the intriguing possibility that the well-established coding principles revealed during spatial navigation can also be used to understand flexible decision making in abstract and discrete tasks that are commonplace in the real world when they are based on a cognitive map, such as whom to collaborate with or where to eat. A cognitive map of an environment or task is incredibly powerful because it enables inferences to be made from limited experiences that can dramatically accelerate new learning and even guide novel decisions never faced before. Moreover, similar tasks that share an overall structure can be directly related to one another, thereby facilitating rapid generalization from one task or entity to another. Despite this wide-ranging importance for flexible cognition, we have only a basic understanding of how cognitive maps enable such novel inferences and generalization. Better understanding the mechanisms involved also carry significant clinical implications. Indeed, abnormal inferences, cognitive flexibility, and generalization are thought to core dysfunctions in several psychiatric conditions, ranging from schizophrenia to obsessive compulsive disorder to certain expressions of mood disorder. It follows that developing a mechanistic model of these component processes in humans has the potential to inform principled investigations into biomarkers and treatment targets for these disorders. The goal of this proposal is to develop a new neural model of how cognitive maps of abstract and discrete tasks are represented neurally and used to guide novel inferences during decision making in the human brain. We have developed a new experimental paradigm that induces people to form abstract and discrete cognitive maps (e.g. of social networks) and perform novel inferences during decision making. To develop our model, we will combine computational models of learning and inference in this paradigm with geometric models of neural coding derived from spatial navigation and “representational” and computational functional magnetic resonance imaging analysis methods that allow inferences to be made about the information represented and computations performed in different brain structures, respectively. The insights gained from this research will lead to substantial theoretical advances in models of goal-directed decision making, cognitive flexibility, and memory, with implications for typical and atypical individuals.