Research on implicit processes has revealed problems with awareness categorizations based on nonsignificant results. Moreover, post hoc categorizations result in regression to the mean (RTM), by which aware participants are wrongly categorized as unaware. Using Bayes factors to obtain sensitive evidence for participants’ lack of knowledge may deal with nonsignificance being nonevidential, but also may prevent regression-to-the-mean effects. Here, we examine the reliability of a novel Bayesian awareness categorization procedure. Participants completed a reward learning task followed by a flanker task measuring attention towards conditioned stimuli. They were categorized as B_Aware and B_Unaware of stimulus–outcome contingencies, and those with insensitive Bayes factors were deemed B_Insensitive. We found that performance for B_Unaware participants was below chance level using unbiased tests. This was further confirmed using a resampling procedure with multiple iterations, contrary to the prediction of RTM effects. Conversely, when categorizing participants using t tests, t_Unaware participants showed RTM effects. We also propose a group boundary optimization procedure to determine the threshold at which regression to the mean is observed. Using Bayes factors instead of t tests as a post hoc categorization tool allows evaluating evidence of unawareness, which in turn helps avoid RTM. The reliability of the Bayesian awareness categorization procedure strengthens previous evidence for implicit reward conditioning. The toolbox used for the categorization procedure is detailed and made available. Post hoc group selection can provide evidence for implicit processes; the relevance of RTM needs to be considered for each study and cannot simply be assumed to be a problem.

对内隐过程的研究揭示了基于不显着结果的意识分类问题。此外，事后分类会导致回归均值 (RTM)，从而将有意识的参与者错误地归类为不知道。使用贝叶斯因子来获取参与者缺乏知识的敏感证据可能会处理无证据的不显着性，但也可能会防止回归均值效应。在这里，我们检查了一种新的贝叶斯意识分类程序的可靠性。参与者完成了一项奖励学习任务，然后是一项测量对条件刺激的注意力的侧翼任务。他们被归类为B _Aware 和B _Unaware of stimulus-outcomeingencies, 那些具有不敏感贝叶斯因素的被认为是B _不敏感。我们发现B _Unaware 参与者的表现低于使用无偏测试的机会水平。使用具有多次迭代的重采样程序进一步证实了这一点，这与 RTM 效应的预测相反。相反，当使用t检验对参与者进行分类时，t_ Unaware 参与者表现出 RTM 效应。我们还提出了一个组边界优化程序来确定观察到均值回归的阈值。使用贝叶斯因子代替t测试作为事后分类工具允许评估不知情的证据，这反过来有助于避免 RTM。贝叶斯意识分类程序的可靠性加强了先前关于隐性奖励条件反射的证据。用于分类过程的工具箱是详细的并可供使用。事后群体选择可以为隐性过程提供证据；每个研究都需要考虑 RTM 的相关性，不能简单地假设它是一个问题。