Department of Hospital Medicine, University of Chicago, USA.
Cogn Sci. 2012 Jul;36(5):919-32. doi: 10.1111/j.1551-6709.2012.01253.x. Epub 2012 May 16.
We investigated the understanding of causal systems categories--categories defined by common causal structure rather than by common domain content--among college students. We asked students who were either novices or experts in the physical sciences to sort descriptions of real-world phenomena that varied in their causal structure (e.g., negative feedback vs. causal chain) and in their content domain (e.g., economics vs. biology). Our hypothesis was that there would be a shift from domain-based sorting to causal sorting with increasing expertise in the relevant domains. This prediction was borne out: the novice groups sorted primarily by domain and the expert group sorted by causal category. These results suggest that science training facilitates insight about causal structures.
我们调查了大学生对因果系统类别（由共同因果结构定义的类别，而不是由共同领域内容定义的类别）的理解。我们要求物理科学领域的新手和专家学生对描述具有不同因果结构（例如负反馈与因果链）和不同内容领域（例如经济学与生物学）的真实世界现象进行分类。我们的假设是，随着相关领域专业知识的增加，分类方式将从基于领域的分类转变为基于因果关系的分类。这一预测得到了验证：新手组主要根据领域进行分类，而专家组则根据因果类别进行分类。这些结果表明，科学训练有助于深入了解因果结构。
