Causal And Conceptual Knowledge

因果知识和概念知识

• 批准号: 6685264
• 负责人: WOO-KYOUNG AHN
• 金额: $ 24.53万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-08-10 至 2005-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6685264
• 关键词: association learning; behavioral /social science research tag; clinical research; cognition; concept; experience; human subject; learning; memory; questionnaires; thinking

DESCRIPTION (provided by applicant): The objectives of the current research program are to understand how people learn causal and conceptual knowledge, and how causal and conceptual knowledge interact with each other. In particular, the proposed project examines two types of concepts that would be influential in causal induction. The first type is people's concepts about structural characteristics of complex causal relations. One such example is the conditional independence assumption in Bayesian Networks, which states that in a causal chain of X causing Y and Y causing Z, X is not predictive of Z once the value of Y is known. Given this assumption, the contingency between X and Z in the above causal chain becomes the product of the contingency between X and Y and the contingency between Y and Z. The first specific aim is to test whether people follow this product rule when they are presented only with piecemeal covariations (e.g., covariation between X and Y, and covariation between Y and Z) and combine them into a causal chain. The second type of prior concept that would be influential in causal induction is knowledge people have about specific events or objects. It is hypothesized that during sequential presentations of covariation information, people initially form a hypothesis about causal relations between specific events presented during the learning phase and interpret later data in light of this initial hypothesis. Consequently, people would be more influenced by data presented early on during learning of a causal relation than by data presented later in the same learning phase, resulting in a primacy effect. Thus, an overarching theme in this proposal is that people apply prior concepts when learning new causal relations both at an abstract level (e.g., constraints imposed on causal structures regardless of the content of specific events) as well as at a specific level (e.g., concepts about causal efficacy of specific events). Understanding causal and conceptual knowledge has important health implications because laypeople as well as clinicians often form causal models for disorders and their treatments, and these models greatly influence health-related decisions involving preventive actions and treatment plans. The aim is to go beyond mere demonstrations of the use of background knowledge in causal induction and to examine the specific nature of processes in which background concepts influence causal induction.

描述（由申请人提供）：当前研究计划的目标是了解人们如何学习因果和概念知识，以及因果和概念知识如何相互作用。特别是，拟议中的项目探讨了两种类型的概念，这将是有影响力的因果归纳。第一类是人们对复杂因果关系结构特征的认识。一个这样的例子是贝叶斯网络中的条件独立假设，它指出，在X导致Y和Y导致Z的因果链中，一旦Y的值已知，X就不能预测Z。在这个假设下，上述因果链中X和Z之间的偶然性就变成了X和Y之间的偶然性与Y和Z之间的偶然性的乘积。第一个具体的目标是测试人们是否遵循这个产品规则时，他们只提出了零碎的协变（例如，X和Y之间的协变，以及Y和Z之间的协变），并将它们联合收割机组合成因果链。在因果归纳中有影响力的第二类先验概念是人们对特定事件或物体的知识。据推测，在顺序介绍的协变信息，人们最初形成一个假设，在学习阶段提出的特定事件之间的因果关系，并解释后来的数据在这个初始假设。因此，人们在学习因果关系的早期阶段受到的影响要比在同一学习阶段后期出现的数据更大，从而导致了首因效应。因此，该提案中的一个首要主题是，人们在学习新的因果关系时，在抽象层面（例如，无论特定事件的内容如何都对因果结构施加的约束）以及在特定级别（例如，关于特定事件的因果效力的概念）。了解因果和概念知识具有重要的健康意义，因为外行以及临床医生往往形成疾病及其治疗的因果模型，这些模型极大地影响了涉及预防行动和治疗计划的健康相关决策。其目的是超越纯粹的示范使用的背景知识在因果归纳和审查的具体性质的过程中，背景概念影响因果归纳。