Kang Youngjin
Department of Psychology, New Mexico State University, Las Cruces, New Mexico1.
Adv Cogn Psychol. 2019 Jun 30;15(2):143-154. doi: 10.5709/acp-0260-3. eCollection 2019.
Extant research suggests that the desirability of an outcome influences the way an individual makes a prediction. The current research investigated how an outcome's desirability influences the extent to which an individual evaluates its probability when making a prediction. Two studies were conducted using a single binary prediction based on the urn model. Individuals predicted which color-red or blue-a ball drawn from a bag would be, while being aware of the proportion of each color in the bag. The results of the first study indicated that individuals predicted the more probable outcome regardless of the probabilities of two outcomes. However, when the less probable outcome was more desirable, the proportion of predictions became significantly correlated and better calibrated to the actual probability. This result was interpreted as showing that, when motivated to predict the more desirable but less probable outcome, individuals evaluate its probability more effortfully. This interpretation was tested in the second study. When the probabiity- matching motivation was implemented, the proportion of individuals who predicted the less probable outcome increased significantly. However, when the less probable outcome was more desirable, the same motivation did not significantly increase the proportion of such individuals. Taken together, these results imply that individuals likely process the same probability informatio differently based on whether this information is useful for predicting a desirable outcome.
现有研究表明,结果的可取性会影响个体进行预测的方式。当前研究调查了结果的可取性如何影响个体在进行预测时评估其概率的程度。使用基于瓮模型的单一二元预测进行了两项研究。个体预测从袋子中抽出的球是红色还是蓝色,同时知晓袋子中每种颜色的比例。第一项研究的结果表明,无论两种结果的概率如何,个体都会预测更有可能的结果。然而,当可能性较小的结果更可取时,预测比例与实际概率显著相关且校准得更好。这一结果被解释为表明,当有动力预测更可取但可能性较小的结果时,个体更努力地评估其概率。在第二项研究中对这一解释进行了检验。当实施概率匹配动机时,预测可能性较小结果的个体比例显著增加。然而,当可能性较小的结果更可取时,相同的动机并没有显著增加此类个体的比例。综合来看,这些结果意味着个体可能会根据该信息是否有助于预测理想结果,对相同的概率信息进行不同的处理。