Toward a Unified Model of Cognitive Control

描述（由申请人提供）： 这项研究的首要目标是了解大脑如何进行认知控制。认知控制指的是认知系统灵活地控制自身行为以应对任务要求或其他突发事件的能力，它倾向于处理与任务相关的信息，而不是处理其他竞争性信息，并调解与任务相关的行为，而不是习惯性或其他优势反应。事实上，人们普遍认为，前额叶皮层（PFC）在认知控制中起着关键作用。然而，就具体的神经机制而言，它到底做了什么以及如何做到这一点，仍然是相当有争议的问题。明确的计算模型，包括生物学机制可以提供一个强大的手段来测试理论思想的生物学基础的认知控制。然而，现有的模型只模拟了一个或几个现象，在整个认知控制领域的普遍适用性留下了悬而未决的问题。我们建议通过应用一个单一的计算模型来解决这一限制，该模型包括基础神经生物学的关键特征（包括基底神经节（BG）及其与PFC的相互作用），以广泛的基准行为和神经现象。这个模型包含了强大的学习机制，可以产生智能的、受控的行为，而不依赖于通常隐藏在认知控制理论背后的那种侏儒。具体目标1：行为和神经数据建模。我们通过评估在单个大型任务语料库上训练的模型的单个实例是否可以模拟认知控制中的各种基准数据来测试我们框架的充分性，并且我们使用该模型进行了一些可测试的预测。具体目标2：PFC/BG表征的性质和学习。我们解决了一个根本问题：当猴子需要几个月的高度集中训练才能学习一项新任务时，人们如何快速适应执行新的认知任务？我们假设，人们在整个漫长的发育期到成年期发展了一套广泛的基本认知操作，并且可以快速灵活地将它们联合收割机结合起来解决新的任务。在明确的计算模型中证明这一原则将对理解人类智力、教育和发展产生重要影响。

DESCRIPTION (provided by applicant): The overarching goal of the proposed research is to understand how the brain performs cognitive control. By cognitive control, we mean the ability of the cognitive system to flexibly control its own behavior in response to task demands or other contingencies, favoring the processing of task-relevant information over other sources of competing information, and mediating task-relevant behavior over habitual or otherwise prepotent responses. There is virtually universal agreement that the prefrontal cortex (PFC) plays a critical role in cognitive control. However, exactly what it does and how it does it, in terms of concrete neural mechanisms, remain considerably more controversial questions. Explicit computational models that incorporate biological mechanisms can provide a powerful means of testing theoretical ideas about the biological basis of cognitive control. However, existing models have each only simulated one or a few phenomena, leaving open questions about their general applicability across the entire domain of cognitive control. We propose to address this limitation by applying a single computational model that includes critical features of the underlying neurobiology (including the basal ganglia (BG) and its interactions with the PFC) to a wide range of benchmark behavioral and neural phenomena. This model includes powerful learning mechanisms that should produce intelligent, controlled behavior without relying on the kind of homunculus that often lies behind theories of cognitive control. Specific Aim 1: Modeling Behavioral and Neural Data. We test the sufficiency of our framework by evaluating whether a single instantiation of the model, trained on a single large corpus of tasks, can simulate a wide range of benchmark data in cognitive control, and we use this model to make a number of testable predictions. Specific Aim 2: Nature and Learning of PFC/BG Representations. We address the fundamental question: how can people quickly adapt to performing novel cognitive tasks, when it takes monkeys months of highly-focused training to learn a single new task? We hypothesize that people develop an extensive repertoire of basic cognitive operations throughout the long developmental period into adulthood, and can rapidly and flexibly combine them to solve novel tasks. Demonstrating this principle in an explicit computational model will have important implications for understanding human intelligence, education, and development.