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专业知识与基于类别的归纳。

Expertise and category-based induction.

作者信息

Proffitt J B, Coley J D, Medin D L

机构信息

Department of Psychology, Northwestern University, Evanston, Illinois 60207-2710, USA.

出版信息

J Exp Psychol Learn Mem Cogn. 2000 Jul;26(4):811-28. doi: 10.1037//0278-7393.26.4.811.

Abstract

The authors examined inductive reasoning among experts in a domain. Three types of tree experts (landscapers, taxonomists, and parks maintenance personnel) completed 3 reasoning tasks. In Experiment 1, participants inferred which of 2 novel diseases would affect "more other kinds of trees" and provided justifications for their choices. In Experiment 2, the authors used modified instructions and asked which disease would be more likely to affect "all trees." In Experiment 3, the conclusion category was eliminated altogether, and participants were asked to generate a list of other affected trees. Among these populations, typicality and diversity effects were weak to nonexistent. Instead, experts' reasoning was influenced by "local" coverage (extension of the property to members of the same folk family) and causal-ecological factors. The authors concluded that domain knowledge leads to the use of a variety of reasoning strategies not captured by current models of category-based induction.

摘要

作者研究了某一领域专家的归纳推理能力。三类树木专家(园艺师、分类学家和公园维护人员)完成了三项推理任务。在实验1中,参与者推断两种新出现的疾病中哪一种会影响“更多其他种类的树木”,并为自己的选择提供理由。在实验2中,作者修改了指示,询问哪种疾病更有可能影响“所有树木”。在实验3中,完全取消了结论类别,要求参与者列出其他受影响树木的清单。在这些群体中,典型性和多样性效应微弱或不存在。相反,专家的推理受到“局部”覆盖范围(属性扩展到同一民俗家族的成员)和因果生态因素的影响。作者得出结论,领域知识导致使用当前基于类别的归纳模型未涵盖的多种推理策略。

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