CAUSAL BACKGROUND KNOWLEDGE EFFECT ON CATEGORIZATION

因果背景知识对分类的影响

• 批准号: 6258292
• 负责人: WOO-KYOUNG AHN
• 金额: $ 10.4万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-04-01 至 2001-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6258292
• 关键词: attribution; behavioral /social science research tag; bias; clinical research; cognition; computational neuroscience; concept; human subject; learning; learning transfer; neural information processing; psychological models; questionnaires

DESCRIPTION (Applicant's Abstract): Nineteen experiments investigate the role of causal background knowledge in determining feature centrality in people's conceptual representations. The main hypothesis is based on a recent theory-based view which suggests that concepts, like theories, have features that are causally connected to each other. Ahn proposes the causal status hypothesis which states that features serving as causes for other features should be more essential than those serving as effects. The proposal describes three sets of studies designed to test and improve this causal status model which automatically determines weights of features based on their causal status. First, the causal status model is applied to account for numerous existing phenomena demonstrating the effect of background knowledge. These include the basic level shift as a function of expertise, differences between natural kinds and artifacts, developmental trends in the ways children treat natural kinds and artifacts, category variability on categorization, and the types of properties in category-based induction. Second, a computational model of the causal status hypothesis is implemented and tested by varying the factors which are predicted to affect feature weighing. These include causal strengths between causally related features, the number of features caused by a target feature, and the number of causal links branching out from a target feature. Thus, the model will provide a basis for predicting feature weighing in complex knowledge bases which have multiple interwoven causal links varying in strengths. Third, the model is tested to explain clinicians' diagnosis processes to investigate not only the model's generality in a sample complex knowledge base but also how extensive use of categories and knowledge on feature probabilities might interact with the causal status bias. The proposed experiments rely on two methods;(1) Tasks using artificial categories directly manipulate causal status of novel features and collect participants ratings on feature centrality for causal and non-causal features and (2) tasks using familiar categories measure participants' existing knowledge on causal status of features which will be subsequently correlated with their centrality ratings. The major theoretical contribution of the model is to rigorously define theory-based categorization which can be applied to real-life cases. In addition, an understanding of conceptual cores in terms of people's causal explanations will elucidate the structure and acquisition of knowledge in general.

描述（申请人的摘要）：十九个实验研究了 因果背景知识在确定特征中心性中的作用 人们的概念表达。 主要假设是基于 最近的一种基于理论的观点认为，概念，如理论， 彼此之间有因果关系的特征。 Ahn提出了因果关系 状态假设，它指出作为其他原因的特征 特征应该比作为效果的特征更重要。 的 该提案描述了三组旨在测试和改进这一点的研究 因果状态模型，自动确定基于 他们的因果关系 首先，因果状态模型被应用于 解释了许多现有的现象，证明了 背景知识。 其中包括基本水平的变化， 专业知识，自然种类和人工制品之间的差异，发展 儿童对待自然种类和人工制品的方式的趋势， 分类的可变性，以及基于类别的属性类型 诱导 第二，因果状态假设的计算模型是 通过改变预测会影响的因素来实施和测试 特征加权。 这些包括因果关系之间的因果强度 特征、由目标特征引起的特征数以及 从目标特征分支出来的因果联系。 因此，该模型将 为预测复杂知识库中的特征权重提供基础 它们之间有着多种相互交织的因果联系，其强度各不相同。 第三、 该模型被用来解释临床医生的诊断过程， 不仅研究模型在复杂知识样本中的通用性， 基础，而且如何广泛使用的类别和知识的功能 概率可能与因果状态偏差相互作用。 拟议 实验依赖于两种方法：（1）使用人工类别的任务 直接操纵新特征的因果状态， 对因果和非因果特征的特征中心性的评级，以及（2） 使用熟悉类别的任务测量参与者对以下方面的现有知识： 特征的因果状态，随后将与其 中心性评级。 该模型的主要理论贡献是 严格定义基于理论的分类，可应用于 现实生活中的案例 此外，理解概念的核心， 人们的因果解释将阐明结构和收购 一般的知识。