Children's Causal Learning and Developing Knowledge of Mechanisms

儿童的因果学习和发展机制知识

• 批准号: 0518161
• 负责人: David Sobel
• 金额: --
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0518161&HistoricalAwards=false
• 关键词: Children; Causal; Learning; Developing; Knowledge

The world is full of causal knowledge that children must discover. While there is little doubt that children learn physical, psychological, and biological knowledge at an amazing capacity, there is only a modest understanding of how children represent this causal knowledge or learn new causal relations. With support from the National Science Foundation, Dr. David Sobel examines the idea that a particular computational framework serves as a model for representing and learning causal knowledge. Preliminary investigations for this study have demonstrated that preschoolers engage in causal inferences according to the premises of this model. Dr. Sobel seeks to extend these findings to toddlers and infants. This would demonstrate that children possess sophisticated causal reasoning abilities from very early ages and would map out a description of how children represent their causal knowledge. Dr. Sobel also suggests a particular algorithm that describes how causal learning takes place. He will investigate children's use of this algorithm when learning both physical and psychological knowledge. This would demonstrate why particular developmental differences in causal learning abilities are present during the infant and preschool years.Much recent research and contemporary thinking suggests that children take an active role in constructing knowledge of their environment. Exactly how children do this is still a mystery. Understanding how children represent and learn causal knowledge should enable researchers to determine how to promote such learning. This would allow us to design better interventions for education, particularly for science education. Understanding how environmental factors contribute to causal learning would shed insight into how negative factors could be reduced. This could prevent children from becoming at-risk learners early in life. Finally, many developmental disorders have been diagnosed by citing that children lack particular causal reasoning abilities (such as psychological knowledge in the case of autism). Understanding how children acquire such causal knowledge might enable us to determine why children with autism (and other at-risk populations) do not acquire such cognitive abilities.

这个世界充满了儿童必须发现的因果知识。毫无疑问，儿童以惊人的能力学习生理、心理和生物知识，但对于儿童如何表现这些因果知识或学习新的因果关系，人们只有一个微不足道的理解。在国家科学基金会的支持下，大卫·索贝尔博士研究了特定的计算框架作为表示和学习因果知识的模型的想法。这项研究的初步调查表明，学龄前儿童根据这一模型的前提进行因果推理。索贝尔博士试图将这些发现推广到幼儿和婴儿身上。这将证明儿童在很小的时候就具有复杂的因果推理能力，并将绘制出儿童如何表示他们的因果知识的描述。索贝尔博士还提出了一种描述因果学习过程的特殊算法。他将调查儿童在学习生理和心理知识时使用这种算法的情况。这将解释为什么在婴幼儿和学龄前阶段，因果学习能力会出现特殊的发展差异。许多最近的研究和当代思维表明，儿童在构建对环境的知识方面发挥了积极的作用。孩子们到底是如何做到这一点的，仍然是一个谜。了解儿童如何表现和学习因果知识应该使研究人员能够确定如何促进这种学习。这将使我们能够为教育，特别是科学教育设计更好的干预措施。了解环境因素如何促进因果学习，将有助于深入了解如何减少负面因素。这可以防止儿童在早期成为有风险的学习者。最后，许多发育障碍被诊断为儿童缺乏特殊的因果推理能力(例如自闭症的心理学知识)。了解儿童是如何获得这种因果知识的，可能使我们能够确定为什么患有自闭症的儿童(和其他高危人群)不能获得这种认知能力。