The Role of Attentional Control in Early Mathematical Learning

该项目由加州大学伯克利分校的一组研究人员领导，旨在了解注意力与早期数学发展之间的关系。儿童最初的数学技能，如计数和数字估计，通常是通过需要儿童保持注意力的任务来衡量的。在某些情况下，他们的数学知识与注意力过程有着内在的联系。例如，在数数时，儿童必须学会有选择地注意一组物体中的每个物体，并按顺序这样做。然后，他们必须为选择的每个对象说出不同的单词。尽管注意力在这些早期数学技能中很重要，但它在支持或限制早期数学发展中所起的确切作用尚不清楚。本提案中描述的研究将涉及针对2至8岁儿童的注意力与数学技能相结合的测量任务，以阐明注意力在早期数学中所起的作用。它将有助于指出某些数学技能的困难是否可以追溯到潜在注意力系统的差异。研究人员将招募高和低SES（社会经济地位）的儿童，以了解SES在数学认知方面的差异是否可以追溯到这些系统，从而为开发基于认知的干预措施提供必要的第一步，这可能会提高这些群体的成就。该项目由EHR核心研究（ECR）项目资助，该项目支持推进STEM学习基础研究文献的工作。数数和近似数估计这两项被广泛研究的数字技能，都依赖于适当地分配注意力资源，以达到对数量的心理表征。然而，许多认知理论和实验研究将这些能力的测量视为纯粹的数字过程，而不是依赖于注意力等一般认知资源。该项目旨在量化和正式建模注意力在计数和估计中所起的作用，并阐明它在多大程度上解释了个体在数学任务中的表现差异，从而允许进一步研究注意力限制是否会限制年龄和社会经济地位的数学发展。这将有可能显示，例如，儿童在学习数数方面的拖延时间有多少可能是由于等待潜在的注意力系统发展。如果成功，这项工作将进一步帮助建议对非数字认知技能的干预措施可能对改善数学教育或潜在的更广泛的STEM教育产生重要影响。研究人员还将创建一个儿童从事数字任务时眼球追踪轨迹的开放数据集，创建分析眼球追踪数据的工具，并形式化描述视觉信息处理如何产生在数字认知中观察到的模式，如主观化、韦伯定律和数字估计偏差。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The project, led by a team of researchers at the University of California, Berkeley, aims to understand the relationship between attention and early mathematical development. Children’s first mathematical skills, like counting and number estimation, are typically measured using tasks that require children to sustain attention. In some cases their knowledge of mathematics is intrinsically linked to attentional processes. For example, in counting, children must learn to attend selectively to each object in a set and do so in sequence. They must then say a different word for each object that is selected. Despite the importance of attention in these early mathematical skills, the precise role that it plays in supporting or constraining early mathematical development is not understood. The research described in this proposal will involve tasks that measure attention in conjunction with mathematical skills for individual children between 2 and 8 years of age in order to clarify the role that attention plays in early mathematics. It will help point to whether difficulties in some mathematical skills may be traceable to differences in underlying attentional systems. The researchers will recruit both high and low SES (socioeconomic status) children in order to understand whether SES differences in mathematical cognition may be traceable to these systems, thereby providing a necessary first-step in developing cognitively-based interventions which may improve achievement in these groups. The project is funded by the EHR Core Research (ECR) program, which supports work that advances the fundamental research literature on STEM learning. Two of the most widely studied numerical skills, counting and approximate number estimation, depend on appropriately deploying attentional resources in order to arrive at a mental representation of quantity. However, many cognitive theories and experimental studies treat measurements of these abilities as reflecting purely numerical processes rather than being dependent on general cognitive resources like attention. This project aims to quantify and formally model the role that attention plays in counting and estimation, and to clarify the degree to which it accounts for individual differences in performance on mathematical tasks, thereby allowing further investigation of whether attentional limitations may constrain mathematical development across age and SES. This will have the potential to show, for example, how much of children’s protracted timeline in learning counting may be due to waiting for the underlying attentional system to develop. If successful, this work will further help suggest what kinds of interventions on non-numerical cognitive skills may have important implications for improving mathematical education or potentially STEM education more broadly. The researchers will also create an open dataset of eye-tracking trajectories as children engage in numerical tasks, create tools for analyzing eye-tracking data, and formalize accounts of how visual information processing may give rise to patterns observed in numerical cognition, like subitizing, Weber’s law, and numerical estimation biases.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.