Hagmayer York, Waldmann Michael R
Department of Psychology, University of Göttingen, Göttingen, Germany.
Q J Exp Psychol (Hove). 2007 Mar;60(3):330-55. doi: 10.1080/17470210601002470.
Estimates of the causal efficacy of an event need to take into account the possible presence and influence of other unobserved causes that might have contributed to the occurrence of the effect. Current theoretical approaches deal differently with this problem. Associative theories assume that at least one unobserved cause is always present. In contrast, causal Bayes net theories (including Power PC theory) hypothesize that unobserved causes may be present or absent. These theories generally assume independence of different causes of the same event, which greatly simplifies modelling learning and inference. In two experiments participants were requested to learn about the causal relation between a single cause and an effect by observing their co-occurrence (Experiment 1) or by actively intervening in the cause (Experiment 2). Participants' assumptions about the presence of an unobserved cause were assessed either after each learning trial or at the end of the learning phase. The results show an interesting dissociation. Whereas there was a tendency to assume interdependence of the causes in the online judgements during learning, the final judgements tended to be more in the direction of an independence assumption. Possible explanations and implications of these findings are discussed.
对某一事件因果效力的估计需要考虑到其他未被观察到的、可能促成该结果发生的原因的存在及其影响。当前的理论方法对这一问题的处理方式有所不同。联想理论假定至少总有一个未被观察到的原因存在。相比之下，因果贝叶斯网络理论（包括幂PC理论）则假设未被观察到的原因可能存在，也可能不存在。这些理论通常假定同一事件的不同原因相互独立，这极大地简化了建模学习和推理过程。在两项实验中，要求参与者通过观察单一原因与结果的同时出现（实验1）或通过主动干预原因（实验2）来了解它们之间的因果关系。在每次学习试验后或在学习阶段结束时，评估参与者对未被观察到的原因是否存在的假设。结果显示出一种有趣的分离现象。在学习过程中的在线判断中，存在假定原因相互依存的倾向，而最终判断则更倾向于独立假设的方向。本文讨论了这些发现的可能解释及其意义。
