The Impact Of States Of Alternative Causes On Causal Inference

Causal inference is one kind of advanced cognitive process which requiresindividual to make the best choice on the basis of the fully understanding of theconstruction, strength and background. Alternative causes refers to others causesexcept focal cause in causal inference between many factors cause one effect.Alternative causes play an important role in causal inference, so we should carefulscrutiny of the content. Studies have showed that the presentation of the causalinformation, causality strength, causal structure and so have much impact on causalinference, then the strength, the structure types and the states of alternative causeshave what effects on the causal inference?Based on the alternative causes, the present study discussed the influence ofcausal reasoning by adjusting the strength of alternative causes, alternative structural,the state of alternative causes.We used three experiments to study.In experiment one, we used2(the strength of focal causes:40%，80%)×3(thestrength of alternative causes:25%，50%，75%) experimental design and the subjectsare university students. In order to avoid the influence of recall errors that wepresented the causal information and the causal structures to the subjects on papersand required the subjects to make probability judgments for P(E/C)，P(C/E)，P(E/-C)，P(C/-E). The research findings showed that predictive inference (from no cause toeffect) is sensitive to the strength of alternative causes; Inference was sensitive to thestrength of focal causes. The results indicated that the subjects can used the inferenceof alternative causes to make causal inference. The neglected of alternative causesduring predictive inferences regardless of the strength of the focal causes.Based on experiment1, in experiment2, we used2(the structures of alternativecauses: implicit, explicit)×3(the strength of alternative causes:25%，50%，75%)experimental design and the subjects are university students.We presented the causalinformation and the causal structures to the subjects on papers and require the subjectsto make probability judgments for P(E/C)，P(C/E)，P(E/-C)，P(C/-E). The researchfindings showed that the alternative causes of class feature do not improve theveracity of predicting inference and it increases the error of strong alternative causes inference; In predicting inference, the subjects were sensitive to the strength ofalternative causes. This showed that reasoner can not integrate the possible states ofthe structure of alternative causes, the subjects may thought that the absence ofinformation implied that it was absent.Based on the findings of experiment1and2, we studied the strength ofalternative causes:25%，50%，75%, by presenting inference questions in which thealternatives causes was specified as definitely present or definitely absent, and thesubjects were also university students. We presented the causal information and thecausal construction to the subjects on papers and require the subjects to makeprobability judgments for P(E/CA)，P(C/EA)，P(E/C-A)，P(C/E-A)，P(E/A)，P(A/E).The research findings showed that the subjects are sensitive to the strength ofalternative reason in predictive inference; Under the condition of absence ofalternative reason, they were sensitive to alternative causes in predicting inference.This showed that people also take the affect of alternative causes into causal inference,that neglecting alternatives is due to errors in representational and computational stepsleadingd up to the final judgment.The study fully investigates the influence of alternative causes on causalinference. We found three conclusions through these three experiments: First, theneglect of alternative causes are not just because of the existing knowledge retrievaldifficult. Second, neglecting alternatives is also not due to strategic laziness, that is,relaxing reasoning norms only when the potential judgment error is small. Third,alternative causes are more likely to be ignored when there is uncertainty about eitherthe state or identity of those causes. The results showed that it easier to make causalinferences when relevant knowledge can be represented simply and concretely.