【作者】 丁小斌；

【导师】 阴国恩；

【作者基本信息】 天津师范大学 ， 基础心理学， 2009， 博士

【摘要】 知识对类别学习与类别使用的影响并不是传统类别研究的内容，只到上世纪80年代，研究者才开始关注个体的原有知识对类别表征和类别使用的影响。归类是类别使用的一种重要形式，归类过程并不只是简单的特征匹配的过程，其中还涉及知识的应用。研究表明知识是通过帮助类别特征之间形成联系而对归类判断产生影响的。因果关系是类别特征之间发生联系的一种重要形式。研究证实特征间因果关系在归类中会产生两种重要的实验效应，因果位置效应和一致性效应。研究者提出依存模型、关系中心假设和因果模型理论三种理论对类别特征因果关系网络中特征在归类判断中的重要性进行解释，不过各种理论均不能完整解释不同因果网络中特征权重的变化。本文认为之所以出现这种结果是因为相关研究以及理论都是立足于特征与类别本质割裂的假设基础上的。本文假设，特征以及类别特征之间包含的因果知识只有在与类别本质发生关联的情况下才具有诊断性价值。在此基础上，本文采用具有现实合理性的人造类别测验的方法，对较为复杂的类别特征因果网络中、不同因果关系强度下以及类别特征因果网络与“类别本质”有无因果联系条件下，各种理论对特征权重变化的预测进行进一步的检验，并尝试结合类别“本质论”观点对实验结果进行解释。本文由三个研究组成，研究一探讨了多因多果链式特征因果网络中特征权重的变化；研究二对因果关系强度和“类别本质”进行操纵，考查因果位置效应和一致性效应随之发生的变化；研究三对实验中几种额外变量的影响进行了检验。研究获得如下结论：1．各种实验条件下均发现了因果位置效应和一致性效应，表明特征在类别特征因果网络中的位置关系和特征之间的交互关系是因果知识在归类中发生作用的两种重要形式。2．复杂类别特征因果网络中因果位置效应的强弱与类别因果网络模式有关。“一因一果链式网络”和“一因多果链式网络”中效应更强，类别特征因果网络中初始原因特征数目的增加削弱因果位置效应。依存模型、关系中心假设和因果模型理论都不能完整解释复杂的类别特征因果网络中特征权重的变化。3．因果位置效应随着因果关系强度的增加单调递增，“类别本质”不仅能增强因果位置效应还能在整体上提升因果网络中特征在归类判断中的重要性。依存模型能够解释因果关系强度变化产生的影响，但无法解释“类别本质”在归类中的作用。4．探讨特征间因果知识对归类的影响时结合特征与类别的关系会大大提高结果的可解释性，在这点上类别“本质论”的观点具有重要的理论意义。更多还原

【Abstract】 The effects of background knowledge on category learning and use has not been a traditional part of the psychology of category but which has slowly grown in popularity since the mid-1980s. Classification is a main form of category use that not always be based on simple matching of properties and that background knowledge is more actively involved. Research has indicated that knowledge helps classification because it relates the features in the category rather than through the properties of the features themselves.A important form relating the features in the category is causal relationship, whicd has been confirmed that interfeature causal relations would trigger two experiments effect in classsification , that is, the causal status effect and the coherence effect. Research also proposed several theories to account for the effect of causal knowledge on classification, that include the dependency model, relational centrality hypothesis and causal model theory, but there are not one theory above could entirely explain the changes of features weight that occurred in different causal network. The article consider that it was the consequence of dissevering the relationship between features and category essence in constructing theory and conducting experiments.The article proposed that the causal knowledge containing in feature and interfeature will not have any diagnosis value in classification unless it connect to the essence of category, and on this basis, the article followed the approach of the testing of artificial (but realistic-sounding) categories to farther test the changes of features weight that occurred in complicated causal network, or caused by the change of causal relationship strength, or take place in weather or not there are the causal relationship exists between features and category essence. The article also attempt to integrate the view of essentialism of categoty with other theories in explaining the experimental results. The article consist of three studies. Study one investigate the changes of features weight at the "multi-cause and multi-effect chain network". Study two manipulate the strength of causal relationship and category essence to observe what effect the two would broght to the causal status effect and the coherence effect. Study three inspect the probable interference caused by extra variable of experimental process.The main conclusion the research obtained as follow:1. The causal status effect and the coherence effect emerge in almost all of experimental conditions of the study, this confirm that position relation and interactive relationship among category features is two important form that causal knowledge implement its effect.2. The pattern of category features causal network effects the strength of the causal status effect and the coherence effect in complicated causal network. The two effects can be enhanced in "one-cause and one-effect chain network" and "one-cause and multi-effect chain network", and the "multi-cause" impair the strength of the causal status effect. The dependency model, relational centrality hypothesis and causal model theory could not entirely explain the changes of features weight that occurred in complicated causal network also.3. The size of the causal status effect increase monotonically with causal strength. Category essence not only enhance the the size of the causal status effect but also whole promote the weight of features in causal network. The dependency model can explain the effect brought about by the change of causal strength, but can not explain the role of category essence in classification.4. Combining the relationship between features and category will greatly improve the interpretability of experiment results, in this point, the view of essentialism of categoty have important theoretical significance.更多还原