Department of Psychology, Stony Brook University, Stony Brook, NY 11794-25001, USA.
J Exp Psychol Learn Mem Cogn. 2011 May;37(3):568-87. doi: 10.1037/a0022970.
In existing models of causal induction, 4 types of covariation information (i.e., presence/absence of an event followed by presence/absence of another event) always exert identical influences on causal strength judgments (e.g., joint presence of events always suggests a generative causal relationship). In contrast, we suggest that, due to expectations developed during causal learning, learners give varied interpretations to covariation information as it is encountered and that these interpretations influence the resulting causal beliefs. In Experiments 1A-1C, participants' interpretations of observations during a causal learning task were dynamic, expectation based, and, furthermore, strongly tied to subsequent causal judgments. Experiment 2 demonstrated that adding trials of joint absence or joint presence of events, whose roles have been traditionally interpreted as increasing causal strengths, could result in decreased overall causal judgments and that adding trials where one event occurs in the absence of another, whose roles have been traditionally interpreted as decreasing causal strengths, could result in increased overall causal judgments. We discuss implications for traditional models of causal learning and how a more top-down approach (e.g., Bayesian) would be more compatible with the current findings.
在现有的因果推理模型中，4 种共变信息（即事件的存在/缺失，随后是另一个事件的存在/缺失）总是对因果强度判断产生相同的影响（例如，事件的共同存在总是暗示生成性因果关系）。相比之下，我们认为，由于在因果学习过程中产生的预期，学习者会根据遇到的共变信息进行不同的解释，而这些解释会影响最终的因果信念。在实验 1A-1C 中，参与者在因果学习任务中对观察结果的解释是动态的、基于预期的，并且与后续的因果判断密切相关。实验 2 表明，添加传统上被解释为增加因果强度的事件共同缺失或共同存在的试验，可能会导致整体因果判断的降低，而添加一个事件在另一个事件缺失的情况下发生的试验，传统上被解释为降低因果强度，可能会导致整体因果判断的增加。我们讨论了这些发现对传统因果学习模型的影响，以及更自上而下的方法（例如贝叶斯方法）如何更符合当前的发现。
