Department of Artificial Intelligence, University of Groningen, The Netherlands.
Cogn Affect Behav Neurosci. 2011 Sep;11(3):344-53. doi: 10.3758/s13415-011-0048-8.
We investigated whether mindfulness training (MT) influences information processing in a working memory task with complex visual stimuli. Participants were tested before (T1) and after (T2) participation in an intensive one-month MT retreat, and their performance was compared with that of an age- and education-matched control group. Accuracy did not differ across groups at either time point. Response times were faster and significantly less variable in the MT versus the control group at T2. Since these results could be due to changes in mnemonic processes, speed-accuracy trade-off, or nondecisional factors (e.g., motor execution), we used a mathematical modeling approach to disentangle these factors. The EZ-diffusion model (Wagenmakers, van der Maas, & Grasman, Psychonomic Bulletin & Review 14:(1), 3-22, 2007) suggested that MT leads to improved information quality and reduced response conservativeness, with no changes in nondecisional factors. The noisy exemplar model further suggested that the increase in information quality reflected a decrease in encoding noise and not an increase in forgetting. Thus, mathematical modeling may help clarify the mechanisms by which MT produces salutary effects on performance.
我们考察了正念训练(MT)是否会影响处理复杂视觉刺激的工作记忆任务中的信息处理。参与者在参加为期一个月的密集 MT 静修(T1)之前和之后(T2)接受了测试,并将他们的表现与年龄和教育程度匹配的对照组进行了比较。在任何时间点,两组的准确率都没有差异。与对照组相比,MT 组在 T2 的反应时间更快,且变化幅度明显更小。由于这些结果可能是由于记忆过程、速度-准确性权衡或非决策因素(例如,运动执行)的变化所致,因此我们使用数学建模方法来区分这些因素。EZ 扩散模型(Wagenmakers、van der Maas 和 Grasman,《心理通报与评论》14:(1),3-22,2007)表明,MT 可提高信息质量并降低反应保守性,而非决策因素不变。嘈杂范例模型进一步表明,信息质量的提高反映了编码噪声的降低,而不是遗忘的增加。因此,数学建模可能有助于阐明 MT 对表现产生有益影响的机制。