Department of Humanities and Social Sciences and Computation, California Institute of Technology, 1200 E. California Blvd, HSS 228-77, Pasadena, CA 91125, USA; Neural Systems Program at the California Institute of Technology, 1200 E. California Blvd, HSS 228-77, Pasadena, CA 91125, USA.
Center for Molecular and Behavioral Neuroscience, Rutgers University - Newark, 197 University Avenue, Newark, NJ 07102, USA.
Trends Cogn Sci. 2020 Mar;24(3):228-241. doi: 10.1016/j.tics.2019.12.016. Epub 2020 Feb 3.
Naturalistic observations show that decisions to avoid or escape predators occur at different spatiotemporal scales and that they are supported by different computations and neural circuits. At their extremes, proximal threats are addressed by a limited repertoire of reflexive and myopic actions, reflecting reduced decision and state spaces and model-free (MF) architectures. Conversely, distal threats allow increased information processing supported by model-based (MB) operations, including affective prospection, replay, and planning. However, MF and MB computations are often intertwined, and under conditions of safety the foundations for future effective reactive execution can be laid through MB instruction of MF control. Together, these computations are associated with distinct population codes embedded within a distributed defensive circuitry whose goal is to determine and realize the best policy.
自然观察表明,避免或逃避捕食者的决策发生在不同的时空尺度上,并且由不同的计算和神经回路支持。在极端情况下,近端威胁通过有限的反射和短视动作来解决,反映了决策和状态空间的减少以及无模型 (MF) 架构。相反,远程威胁允许基于模型的 (MB) 操作支持的信息处理增加,包括情感预测、重放和规划。然而,MF 和 MB 计算通常交织在一起,在安全条件下,通过 MB 指令对 MF 控制进行指导,可以为未来有效的反应执行奠定基础。这些计算共同与分布式防御电路中的嵌入的独特群体代码相关联,其目标是确定和实现最佳策略。