The integration of causal knowledge

因果知识的整合

• 批准号: 7000140
• 负责人: RUSSELL C BURNETT
• 金额: $ 4.4万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-01 至 2009-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7000140
• 关键词: behavioral /social science research tag; child psychology; clinical research; cognition; comprehension; human subject; learning; middle childhood (6-11); postdoctoral investigator; preschool child (1-5); psychological models; university student

DESCRIPTION (provided by applicant): Reasoning about complex causal systems is central to everyday cognition, and yet our knowledge of such systems is often incoherent, in that individual (or small sets of) causal relationships are not integrated to form more comprehensive mental models. Two factors are hypothesized to have large roles in determining when, why, and how causal knowledge is integrated: (a) the use of correlational information to distinguish among multiple possible integrated models and (b) the use of fragmentary knowledge in reasoning. Two studies examine how adults use correlational information in integration. These studies distinguish between a hypothesis derived from Bayesian network theory and an alternative hypothesis in which learners interprete correlational information in a way that enables them to acquire incomplete or shallow models. Two further studies track the development of these abilities in young children. Finally, it is hypothesized that using fragmentary knowledge for different kinds of reasoning leads to different kinds of integrated models. This hypothesis is tested in two studies.

关于复杂因果系统的推理是日常认知的核心，但我们对这些系统的知识往往是不连贯的，因为个体（或小集合）因果关系没有被整合以形成更全面的心理模型。 两个因素被假设有很大的作用，在确定何时，为什么，以及如何因果关系的知识是集成：（a）使用相关信息，以区分多个可能的集成模型和（B）使用零碎的知识推理。两项研究探讨了成年人如何在整合中使用相关信息。这些研究区分了贝叶斯网络理论的假设和另一种假设，在这种假设中，学习者以一种使他们能够获得不完整或浅层模型的方式来获取相关信息。两项进一步的研究跟踪了幼儿这些能力的发展。最后，它是假设，使用碎片知识的不同类型的推理导致不同类型的集成模型。这一假设在两项研究中得到验证。