Data on the likelihood of becoming a professional athlete are abundant and readily available, yet athletes consistently overestimate their chances of achieving the top levels of career success. Research is needed to examine whether athletes and others update their career expectations when seeing new information. In this study, minor league baseball players created a career tree estimating their probabilities of moving through the minor league system and then read personalized trees built by a C5.0 machine learning algorithm. After seeing the C5.0 trees, many players updated their expectations consistent with updating theory, especially when reevaluating their chances of being out of the system; however, there was evidence of asymmetric updating. Some acted opposite to what Bayesian reasoning would suggest. Analysis of the interview data reveals three themes that explain asymmetric and contrary updating. Players believed optimism is necessary for their baseball career, they neglected their reference group, and they saw information as possessing affective qualities. Using these three themes caused athletes to ignore some information and, occasionally, circumvent the updating process altogether.

关于成为职业运动员的可能性的数据丰富且易于获得，但运动员始终高估了他们获得职业成功最高水平的机会。需要进行研究以检查运动员和其他人在看到新信息时是否会更新他们的职业期望。在这项研究中，小联盟棒球运动员创建了一个职业树，估计他们在小联盟系统中移动的概率，然后读取由 C5.0 机器学习算法构建的个性化树。很多玩家看到C5.0的树后，更新了与更新理论一致的预期，尤其是在重新评估自己退出系统的机会时；然而，有证据表明不对称更新。有些人的行为与贝叶斯推理所暗示的相反。对访谈数据的分析揭示了解释不对称和相反更新的三个主题。球员们相信乐观对于他们的棒球生涯是必要的，他们忽视了他们的参考群体，他们认为信息具有情感品质。使用这三个主题会导致运动员忽略一些信息，有时甚至完全绕过更新过程。