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ISSN : 1226-9654
The Effect of a Functional form of Causal relation on Accurate Causal Reasoning
Abstract
The success of an individual, a business and even a country depends on the accuracy of judgments and predictions about the future. Reasoners, however, often fail to search for additional pieces of information to make an accurate prediction. Moreover, they tend to neglect a relevant piece of information even if it is available. Thus, it is critical to understand the cognitive process behind the neglect phenomenon in order to increase the accuracy of predictions. The present study aimed to investigate the process that underlies neglect of relevant information based on a newly emerging normative model, the causal Bayes nets. In Experiment 1, we constructed a situation where the three boundary conditions of the causal Bayes nets were satisfied: the sufficiency of a causal structure, parameters, and the clarity of the functional form of causal structure. The results revealed that participants did not neglect a relevant piece of information when they made a predictive inference. Experiment 2 was identical to Experiment 1, except for the functional form of causal structure. The causal structure in Experiment 1 was activated by itself while the structure in Experiment 2 was activated only when it was intervened. With the intervention-based causal structure, participants showed neglect of a relevant piece of information to make a predictive inference. Our results suggest that neglect of a functional form of events in question is responsible for the failure to consider relevant information. Our results also suggest that the assumptions that the causal Bayes nets make about representations of causal relations may not be consistent with the actual representations of reasoners. Our results suggest that a future study is necessary to investigate how understanding of the functional form of causal structures influences prediction accuracy.
- keywords
- The causal Bayes nets, neglect of relevant information, predictions, 정보 무시 현상, 인과 베이즈 모형, 인과 구조 작동 방식, 예측 추론
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