Wismer Andrew J, Bohil Corey J
Department of Psychology, University of Central Florida, Orlando, Florida, United States of America.
PLoS One. 2017 Jun 20;12(6):e0179256. doi: 10.1371/journal.pone.0179256. eCollection 2017.
Two experiments assessed the contributions of implicit and explicit learning to base-rate sensitivity. Using a factorial design that included both implicit and explicit learning disruptions, we tested the hypothesis that implicit learning underlies base-rate sensitivity from experience (and that explicit learning contributes comparatively little). Participants learned to classify two categories of simple stimuli (bar graph heights) presented in a 3:1 base-rate ratio. Participants learned either from "observational" training to disrupt implicit learning or "response" training which supports implicit learning. Category label feedback on each trial was followed either immediately or after a 2.5 second delay by onset of a working memory task intended to disrupt explicit reasoning about category membership feedback. Decision criterion values were significantly larger following response training, suggesting that implicit learning underlies base-rate sensitivity. Disrupting explicit processing had no effect on base-rate learning as long as implicit learning was supported. These results suggest base-rate sensitivity develops from experience primarily through implicit learning, consistent with separate learning systems accounts of categorization.
两项实验评估了内隐学习和外显学习对基础概率敏感性的作用。采用包含内隐学习和外显学习干扰的析因设计，我们检验了以下假设：内隐学习是从经验中获得基础概率敏感性的基础（而外显学习的贡献相对较小）。参与者学习对以3:1基础概率呈现的两类简单刺激（柱状图高度）进行分类。参与者通过“观察性”训练来干扰内隐学习，或者通过支持内隐学习的“反应性”训练来学习。每次试验中的类别标签反馈，要么在反馈出现后立即呈现一个旨在干扰对类别成员反馈进行外显推理的工作记忆任务，要么在延迟2.5秒后呈现。在反应性训练后，决策标准值显著更大，这表明内隐学习是基础概率敏感性的基础。只要内隐学习得到支持，干扰外显加工对基础概率学习就没有影响。这些结果表明，基础概率敏感性主要通过内隐学习从经验中发展而来，这与分类的独立学习系统观点一致。
