An Integrated Real-Time Approach to the Development of Human Problem Solving

解决问题在任何时代和文化中都是普遍存在的——如何在混乱的环境中导航，如何使用工具，等等。随着我们的身体、技能和环境的变化，新的问题出现了，需要新的方法来解决它们。求解者不能重复执行相同的习惯性动作，因为现实世界的环境是多变的，不可预测的，并且充满了新奇的情况。拟议项目的总体目标是理解（并干预）人类解决问题技能发展的基础过程。传统上，发展研究采用一种规范的、以结果为导向的方法，通过确定儿童成功解决各种问题的年龄。这种以结果为导向的方法确立了运动问题的解决始于婴儿期，并随着年龄和经验的增长而提高。然而，关键的知识缺口仍然存在：感知、认知和行动如何时刻展开，以实现有效的解决方案？什么经历能促进早期运动问题解决能力？年轻时解决运动问题的能力是否能预测随后的高阶认知技能？为了回答这些问题，我们采用了一种独特的综合方法，结合了发展心理学、神经科学、人工智能、电子工程和运动控制的观点和方法。我们的指导假设是，儿童在多变的世界中不断变化的身体的日常变化，通过校准感知、神经和运动过程的实时交互系统，促进了运动问题解决能力的提高，而这反过来又促进了随后的认知技能。我们将在纵向研究中通过使用对象操作作为模型系统来检验这一假设。本研究的目的1将确定儿童在解决问题时的效率、灵活性、稳定性和通用性如何依赖于实时级联的时间——从收集任务信息，到任务相关信息的神经分化，到相关肌肉的招募，最后是运动执行。目标2将控制儿童在家中玩物品时的各种日常体验，使用独特的定制玩具对其进行量化，并确定早期可变体验对随后解决问题技能的贡献。目标3将评估运动问题解决技能和后续高阶认知技能（包括推理、计划和执行功能）之间的发展关系。通过揭示运动问题解决的起源，本研究对理解与学习相关的基础过程（如计划、组织、注意力、行为灵活性、执行功能和自我调节）具有广泛的意义。确定促进问题解决的因素可以支持实践、计划和政策，以改善儿童的成果，使他们充分发挥潜力，无论他们的背景、家庭收入或居住地点如何。此外，这项工作将提供个人如何实时解决问题的见解，从而区分缺陷的原因是由于缺乏知觉信息、神经处理还是运动灵活性。因此，研究结果可能对理解与问题解决缺陷相关的障碍具有翻译意义，并可能支持教育干预。通过了解个人缺陷的来源，可以根据他们的具体需要进行干预。最后，拟议的工作将通过开放共享丰富的数据、方法（实验协议、机器学习算法、分析脚本）、发现（视频、生理数据）和工程协议（智能玩具），在已建立的存储库中推进学习和发展的国际研究。拟议中的项目有望为STEM和其他学科的研究铺平道路，以解释和促进儿童的成果。

Problem solving is ubiquitous across every age and culture-how to navigate a cluttered environment, use a tool, and so on. As our bodies, skills, and environments change, new problems emerge and require new means to solve them. Solvers cannot perform the same habitual actions over and over by rote because real-world environments are variable, unpredictable, and full of novel situations. The overarching goal of the proposed project is to understand (and intervene on) processes that underlie the development of human problem-solving skills.Traditionally, developmental research takes a normative, outcome-oriented approach by identifying the ages at which children succeed in solving various problems. This outcome-oriented approach established that motor problem solving begins in infancy and improves with age and experience. Yet, critical knowledge gaps exist: how do perception, cognition, and action unfold from moment to moment to enable efficient solutions? What experiences facilitate early motor problem-solving skills? And does motor problem-solving at a younger age predict subsequent high-order cognitive skills?To answer these questions, we adopt a unique integrative approach combining perspectives and methods from developmental psychology, neuroscience, artificial intelligence, electrical engineering, and motor control. Our guiding hypothesis is that the variability in children's daily flux of a changing body in a variable world facilitates motor problem-solving skills by calibrating a real-time interactive system of perceptual, neural, and motor processes, which in turn facilitates subsequent cognitive skills.We will test this hypothesis in a longitudinal study by using object manipulation as a model system. Aim 1 of the proposed research will determine how children's efficiency, flexibility, stability, and generalisability during problem solving rely on the timing of real-time cascade - from gathering information about the task, to neural differentiation of task-relevant information, to the recruitment of relevant muscles, and finally movement execution. Aim 2 will manipulate the variety of children's everyday experiences during object play in their home, quantify it across one year using unique custom-built toys, and determine the contribution of early variable experiences to subsequent problem-solving skills. Aim 3 will assess the developmental relations between motor problem-solving skills and subsequent high-order cognitive skills, including reasoning, planning, and executive functioning.By uncovering the origins of motor problem solving, the proposed work has broad implications for understanding foundational learning-related processes such as planning, organisation, attention, behavioural flexibility, executive function, and self-regulation. Identifying factors that promote problem solving can support practices, programs, and policies to improve children's outcomes and make them reach their full potential, whatever their background, family income, or place of living. Moreover, this work will provide insights into how individuals solve the problem in real-time and thereby distinguish whether causes of deficits result from lack of perceptual information, neural processing, and/or motor dexterity. Thus, findings may have translational implications for understanding disorders related to deficits in problem solving and may support educational interventions. By knowing the source of deficit in individuals, interventions can be tailored to their specific needs.Finally, the proposed work will advance international research on learning and development through open sharing of rich data, methods (experimental protocols, machine-learning algorithms, analysis scripts), findings (videos, physiological data), and engineering protocols (intelligent toys) in established repositories. The proposed project promises to pave the way for research in STEM and other disciplines to explain and promote children's outcomes.