The neurocognitive dynamics of learning and executive function

项目摘要 执行功能(EF)是指控制和调节过程的集合 实现灵活的或目标导向的行为的认知功能。EF的影响是多样的 发展成果，包括生活质量测量和学业成就。 通过这种方式，EF可以作为改善认知的一般方面的杠杆点 通过干预措施发挥作用，最终增强身体和财务健康 降低犯罪率。然而，目前还缺乏可以用来分析的理论模型。 解释EF流程并制定有充分依据的干预措施。这项建议旨在 测试我们先前提出的用于解释中心方面的学习机制 EF发展：维度标签学习假说。根据这一点 假设，学习视觉特征和尺寸(例如，颜色或形状)的标签 结构前后部皮质连通性。然后可以使用这些连接来 通过视觉世界的任务相关特征引导认知加工 标签的激活。为了阐明这一过程，我们将对儿童进行纵向跟踪 从2岁到5岁。将执行一系列任务来评估维度标签 学习和空间注意力。神经活动将从额叶开始测量， 使用功能性近红外光谱分析的颞叶和顶叶皮质。最后， 将使用动态神经场(DNF)模拟来解释神经和 行为数据。这样的模型可以用来实现关于 神经认知功能和学习以迭代的方式评估模型适合性。在这 通过这种方式，可以开发一个模型来解释观察到的行为和神经数据 孩子们学习维度标签和注意力技能。然后，此模型可以提供 用来测试关于学习过程的不同假设 EF的变化。我们将使用这些数据来检查：(1)维度标签是否 学习预测维度注意的发展，(2)空间注意的神经基础 维度标签学习，(3)维度标签过程中的神经动力学 理解和生产预测维度注意任务中的神经激活， (4)DNF模型所实现的学习和神经认知过程 解释行为数据和神经数据之间的联系。DNF模型可以是 用于预测维度标签学习在EF方面的作用 发展。

Project Summary Executive function (EF) refers to the collection of processes that control and regulate cognitive functioning to achieve flexible or goal-directed behavior. EF influences diverse developmental outcomes, including quality of life measures and academic achievement. In this way, EF can serve as a leverage point for improving general aspects of cognitive functioning through interventions, ultimately enhancing physical and financial health and reducing crime rates. However, there is a lack of theoretical models that can be used to explain EF processes and develop well-grounded interventions. This proposal aims to test a learning mechanism that we have previously proposed to explain central aspects of EF development: the dimensional label learning hypothesis. According to this hypothesis, learning labels for visual features and dimensions (e.g, color or shape) structures frontal-posterior cortical connectivity. These connections can then be used to guide cognitive processing toward task-relevant features of the visual world through the activation of labels. To shed light on this process, we will follow children longitudinally from ages 2 to 5. A battery of tasks will be administered to assess dimensional label learning and dimensional attention. Neural activity will be measured from frontal, temporal, and parietal cortices using functional near-infrared spectroscopy. Finally, dynamic neural field (DNF) simulations will be used to interpret the neural and behavioral data. Such models can be used to implement specific hypotheses about neurocognitive functioning and learning to assess model fit in an iterative fashion. In this way, a model can be developed to explain behavioral and neural data observed as children learn dimensional labels and attentional skills. This model can then provide an arena to test different hypotheses about the learning processes that give rise to changes in EF. We will use these data to examine: (1) whether dimensional label learning predicts the development of dimensional attention, (2) the neural basis of dimensional label learning, (3) whether the neural dynamics during dimensional label comprehension and production predict neural activation in dimensional attention tasks, (4) whether the learning and neurocognitive processes implemented by the DNF model explain the association between behavioral and neural data. The DNF model can be used to make predictions about the role of dimensional label learning in aspects of EF development.