维度的结合与分离对归类不确定性预测的影响

INFLUENCE OF ASSOCIATION AND SEPARATION IN THE DIMENSIONS ON THE PREDICTIONS IN THE UNCERTAIN CIRCUMSTANCE OF CLASSIFYING

探讨在归类不确定的情境下目标与预测特征两个维度的结合或分离对被试特征预测的影响。共包括 3个实验 :实验 1在Murphy和Ross的研究的基础上进一步提高非靶类型中目标及预测特征的基本概率 ,考察被试的特征预测是否会受非靶类型信息的影响。实验 2探讨非靶类型的目标与预测特征结合与否是否会影响被试预测特征时对非靶类型信息的使用。实验 3探讨提高靶类型中目标与预测特征结合的比例是否影响被试对特征的预测。结果表明 :当非靶类型中目标与关键特征处于分离的状态时 ,被试在进行特征预测时没有利用非靶类型的信息 ,符合单类说的假设 ;而当非靶类型中目标与关键特征结合时 ,被试在进行特征预测时则会利用非靶类型的信息 ,符合Bayesian规则 ;靶类型中的目标与关键特征结合的比例提高 ,被试对特征预测的概率也随之提高。据此 ,本研究将目标与预测特征结合比例这个变量加入Bayesian规则的计算公式 。

Three experiments were designed to investigate how the subjects were influenced in the course of making feature prediction in the uncertain circumstance of classifying when the subject and the feature were associated or separated Experiment 1 explored whether the subjects used the non-target categories in the course of making feature prediction when the base probability of the feature within the non-target categories was increased The results in Experiment 1a and 1b show that, even though the base probability of feature prediction for non-target categories is enhanced, subjects only consider the target category in their feature probability prediction without considering the information from other non-target categories Experiment 2 explored whether the association of the dimensions within the non-target categories promoted the use of the non-target categories The results prove that, in the case of the association of the two dimensions in non-target categories, the subjects will use the non-target categories information when they are making a feature prediction, which is in conformity with the Bayesian rule Experiment 3 explored whether the feature prediction was influenced when the rate of association of the dimensions within the target category was promoted The results show that the raise of proportion in the association of target and prediction feature in target category will enhance the feature prediction probability The results of the three experiments showed:(1) If the dimensions within the non-target categories were separated, subjects didn't take into account the non-target categories, following the single-category view (2) When the dimensions were associated within the non-target categories, the subjects would use the information of the non-target categories (3) If the proportion of the association in the dimensions within the target category were raised, the probability of the feature prediction would be enhanced According to these results, the proportion of association of the object and the feature should be thought as an important variable, which should be put into the formula of the BayesianRule