The discovery of processing stages: Extension of Sternberg's method.

The discovery of processing stages: Extension of Sternberg's method.
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DOI:
10.1037/rev0000030
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发表时间:
2016-10
影响因子:
5.4
通讯作者:
Walsh, Matthew M.
Walsh, Matthew M.
中科院分区:
心理学1区
文献类型:
--
作者:
Anderson, John R.;Zhang, Qiong;Borst, Jelmer P.;Walsh, Matthew M.

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介绍了一种从脑电信号中测量加工阶段数目和持续时间的方法,并将其应用于联想识别的研究中。该方法将多变量模式分析(MVPA)与隐式半马尔可夫模型(HSMM)相结合,利用过去研究的扩展,在逐个试验的基础上识别正在进行的EEG信号中添加了哪些短暂的正弦波峰(称为凸起)。我们认为,这些突起标志着加工过程中关键认知阶段的开始。分析结果可用于指导详细工艺模型的开发。HsMM-MVPA方法在联想识别任务中的应用表明,联想强度和探测类型的影响局限于记忆提取阶段和决策阶段。这与先前开发的任务的ACT-R过程模型是一致的。作为对我们方法的泛化的测试,我们还将其应用于由收集的关于Sternberg工作记忆任务的数据集。该分析有力地概括了典型的集合规模效应,并将其局限于后期的比较/决策阶段。除了提供关于联想识别阶段的数量和持续时间的信息外,我们的分析还揭示了与识别记忆研究有关的事件相关电位成分。
We introduce a method for measuring the number and durations of processing stages from the electroencephalographic (EEG) signal and apply it to the study of associative recognition. Using an extension of past research that combines multivariate pattern analysis (MVPA) with hidden semi-Markov models (HSMMs), the approach identifies on a trial-by-trial basis where brief sinusoidal peaks (called bumps) are added to the ongoing EEG signal. We propose that these bumps mark the onset of critical cognitive stages in processing. The results of the analysis can be used to guide the development of detailed process models. Applied to the associative recognition task, the HSMM-MVPA method indicates that the effects of associative strength and probe type are localized to a memory retrieval stage and a decision stage. This is in line with a previously developed ACT-R process model of the task. As a test of the generalization of our method we also apply it to a data set on the Sternberg working memory task collected by. The analysis generalizes robustly, and localizes the typical set size effect in a late comparison/decision stage. In addition to providing information about the number and durations of stages in associative recognition, our analysis sheds light on the ERP components implicated in the study of recognition memory.
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