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.
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