White Peter A
School of Psychology, Cardiff University, Cardiff, Wales, UK.
Q J Exp Psychol B. 2005 Apr;58(2):99-140. doi: 10.1080/02724990444000078.
In judging the extent to which a cue causes an outcome, judgement can be affected by information about other cues that are correlated with the one being judged. These cue interaction effects have usually been interpreted in terms of associative learning processes. I propose that a different model of causal judgement, the evidential evaluation model, offers a viable alternative interpretation of cue interaction phenomena. Under the evidential evaluation model, instances of contingency information are interpreted as evidence, which is confirmatory, disconfirmatory, or irrelevant for the cue being judged. When two cues co-occur in a set of instances the evidential value of the instances for one of them is determined by three factors: the proportion of confirming instances in the set; disambiguation value, which concerns the relation between the set of information and prior beliefs about the co-occurring cue; and confirmation value, which concerns the relation between the set of information and prior beliefs about the cue being judged. Any previous judgement of the cue is then modified in the light of these. It is shown that this model can account for all the cue interaction phenomena that have been investigated in studies of human causal judgement. The model also generates novel predictions, and the results of three experiments give support to these predictions. It is also shown that several other current models of causal judgement fail to predict a key result from Experiment 3.
在判断一个线索导致某种结果的程度时，判断可能会受到与被判断线索相关的其他线索信息的影响。这些线索交互效应通常根据联想学习过程来解释。我提出，一种不同的因果判断模型，即证据评估模型，为线索交互现象提供了一种可行的替代解释。在证据评估模型下，偶然性信息的实例被解释为证据，对于被判断的线索来说，这些证据可能是证实性的、证伪性的或无关的。当两个线索在一组实例中共同出现时，这些实例对其中一个线索的证据价值由三个因素决定：该组中证实性实例的比例；消歧价值，它涉及信息集与关于共同出现线索的先验信念之间的关系；以及确认价值，它涉及信息集与关于被判断线索的先验信念之间的关系。然后根据这些因素对线索的任何先前判断进行修正。结果表明，该模型可以解释在人类因果判断研究中所考察的所有线索交互现象。该模型还产生了新的预测，并且三个实验的结果支持了这些预测。研究还表明，其他几种当前的因果判断模型无法预测实验3的一个关键结果。
