Vadillo Miguel A, Ortega-Castro Nerea, Barberia Itxaso, Baker A G
University College London, <location>UK</location>
Universidad de Deusto, Bilbao, <location>Spain</location>
Exp Psychol. 2014;61(5):356-67. doi: 10.1027/1618-3169/a000255.
Many theories of causal learning and causal induction differ in their assumptions about how people combine the causal impact of several causes presented in compound. Some theories propose that when several causes are present, their joint causal impact is equal to the linear sum of the individual impact of each cause. However, some recent theories propose that the causal impact of several causes needs to be combined by means of a noisy-OR integration rule. In other words, the probability of the effect given several causes would be equal to the sum of the probability of the effect given each cause in isolation minus the overlap between those probabilities. In the present series of experiments, participants were given information about the causal impact of several causes and then they were asked what compounds of those causes they would prefer to use if they wanted to produce the effect. The results of these experiments suggest that participants actually use a variety of strategies, including not only the linear and the noisy-OR integration rules, but also averaging the impact of several causes.
许多因果学习和因果归纳理论在关于人们如何整合复合呈现的多个原因的因果影响的假设上存在差异。一些理论提出,当存在多个原因时,它们的联合因果影响等于每个原因的个体影响的线性总和。然而,一些近期的理论提出,多个原因的因果影响需要通过噪声或整合规则来进行整合。换句话说,给定多个原因时效应的概率等于分别给定每个原因时效应的概率之和减去这些概率之间的重叠部分。在本系列实验中,参与者被给予了关于多个原因的因果影响的信息,然后被问及如果他们想要产生该效应,他们会更倾向于使用哪些原因的组合。这些实验的结果表明,参与者实际上使用了多种策略,不仅包括线性和噪声或整合规则,还包括对多个原因的影响进行平均。