Cognitive maps for goal-directed decision making

用于目标导向决策的认知图

• 批准号: 10608120
• 负责人: Erie D Boorman
• 金额: $ 47.06万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10608120
• 关键词: Acceleration; Auditory; Behavior; Behavioral; Biological Markers; Brain; Clinical; Code; Cognition; Cognitive; Collaborations; Computer Models; Data; Decision Making; Dimensions; Diné Nation; Disease; Eating; Environment; Event; Functional Magnetic Resonance Imaging; Functional disorder; Genealogical Tree; Goals; Hippocampus; Human; Hybrids; Individual; Investigation; Knowledge; Learning; Length; Literature; Maps; Marriage; Measures; Memory; Mental disorders; Methods; Modeling; Mood Disorders; Obsessive-Compulsive Disorder; Outcome; Pattern; Persons; Positioning Attribute; Process; Psyche structure; Recording of previous events; Research; Response to stimulus physiology; Rewards; Schizophrenia; Social Network; Stimulus; Structure; Substance abuse problem; System; Task Performances; Testing; Time; Visual; experience; flexibility; insight; learned behavior; neural; neural model; novel; sensory feedback; social; social space; sound frequency; two-dimensional; way finding

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.

摘要 认知地图是指空间和非空间关系的内部表示， 外部世界中的实体（人或事物）或事件。人们普遍 最近发现，即使是连续的非空间任务维度， 可以被组织和“导航”为认知地图。这些研究表明， 在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”任何行为相关的连续任务的通用机制 维度（例如空间、时间、声音频率、公制长度）。这一见解引发了一个耐人寻味的问题， 在空间导航过程中揭示的完善的编码原则也可能 用于理解抽象和离散任务中的灵活决策， 在真实的世界中，当它们基于认知地图时， 合作或在哪里吃饭。 一个环境或任务的认知地图是非常强大的，因为它使 从有限的经验中做出推论，可以大大加快新的学习 甚至指导从未面对过的新决定。此外，共享一个 整体结构可以直接相互关联，从而便于快速概括 从一个任务或实体到另一个。尽管灵活认知具有广泛的重要性， 我们对认知地图如何实现这种新颖的推理只有基本的了解， 一般化更好地理解所涉及的机制也具有重要的临床意义。 含义。事实上，异常推理，认知灵活性和概括被认为是 从精神分裂症到强迫症， 强迫症与情绪障碍的某些表现有关。因此，开发一个 人类这些组成过程的机械模型有可能为我们提供信息， 这些疾病的生物标志物和治疗靶点的原则性研究。 该提案的目标是开发一种新的神经模型，以了解认知如何映射 抽象和离散的任务被神经地表示，并被用于指导新的推理， 人类大脑中的决策。我们开发了一种新的实验模式， 诱导人们形成抽象和离散的认知地图（例如社交网络）， 在决策过程中进行新的推理。为了发展我们的模型，我们将联合收割机 学习和推理的计算模型，在这个范例中， 神经编码源自空间导航和“代表性”和计算 功能性磁共振成像分析方法， 在不同的大脑结构中表现的信息和执行的计算， 分别从这项研究中获得的见解将导致实质性的理论进步 在目标导向决策、认知灵活性和记忆的模型中， 典型和非典型的人。