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Strategic Cognitive Sequencing: A Computational Cognitive Neuroscience Approach

机译:战略性认知测序:一种计算性认知神经科学方法

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We address strategic cognitive sequencing, the "outer loop" of human cognition: how the brain decides what cognitive process to apply at a given moment to solve complex, multistep cognitive tasks. We argue that this topic has been neglected relative to its importance for systematic reasons but that recent work on how individual brain systems accomplish their computations has set the stage for productively addressing how brain regions coordinate over time to accomplish our most impressive thinking. We present four preliminary neural network models. The first addresses how the prefrontal cortex (PFC) and basal ganglia (BG) cooperate to perform trial-and-error learning of short sequences; the next, how several areas of PFC learn to make predictions of likely reward, and how this contributes to the BG making decisions at the level of strategies. The third models address how PFC, BG, parietal cortex, and hippocampus can work together to memorize sequences of cognitive actions from instruction (or "self-instruction"). The last shows how a constraint satisfaction process can find useful plans. The PFC maintains current and goal states and associates from both of these to find a "bridging" state, an abstract plan. We discuss how these processes could work together to produce strategic cognitive sequencing and discuss future directions in this area.
机译:我们解决战略性认知排序问题,这是人类认知的“外环”:大脑如何决定在给定的时间应用什么样的认知过程来解决复杂的,多步骤的认知任务。我们认为,由于系统原因,这个话题相对于它的重要性而被忽略了,但是有关单个大脑系统如何完成其​​计算的最新研究为有效解决大脑区域如何随着时间进行协调以完成我们最令人印象深刻的思想奠定了基础。我们提出了四个初步的神经网络模型。第一个解决了前额叶皮层(PFC)和基底神经节(BG)如何协作执行短序列的反复试验学习的问题;接下来,PFC的几个领域如何学会对可能的回报进行预测,以及这如何有助于BG在策略级别做出决策。第三个模型解决了PFC,BG,顶叶皮层和海马体如何协同工作以记忆来自指令(或“自我指令”)的认知动作序列的问题。最后一个显示了约束满足过程如何找到有用的计划。 PFC维护当前状态和目标状态,并从这两个状态进行关联以找到“桥接”状态,即抽象计划。我们讨论了这些过程如何协同工作以产生战略性认知序列,并讨论了该领域的未来方向。

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