A hierarchical process-dissociation model.
A hierarchical process-dissociation model.
复制标题
分层过程分离模型。
DOI：
10.1037/0096-3445.137.2.370
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发表时间：
2008
期刊：
影响因子：
--
通讯作者：
Speckman,PaulL
中科院分区：
文献类型：
--
作者：
Rouder,JeffreyN;Lu,Jun;Morey,RichardD;Sun,Dongchu;Speckman,PaulL
在拟合过程-分离模型（LL Jacoby, 1991）观察到的数据时，研究人员汇总了参与者、项目或两者的结果。T. Curran和DL . Hintzman（1995）证明了来自聚合的偏差如何导致对模型的人为支持。作者开发了一个层次的过程分离模型，不需要聚合分析。最重要的是，柯伦和欣茨曼的批判并不适用于这个模型。模型分析支持过程解离选择性影响持有，参与者和项目之间的相关模式存在解离。记忆更好的项目也会引发更高的自动激活。然而，在参与者之间没有相关性；也就是说，回忆高的参与者没有增加自动激活的倾向。对聚合的批判并不局限于过程分离。在许多非线性模型中，包括信号检测、多项式处理树模型和强度模型，聚集会导致分析失真。分层建模作为一种通用的解决方案，可以准确地将这些心理处理模型拟合到数据中。
In fitting the process-dissociation model (LL Jacoby, 1991) to observed data, researchers aggregate outcomes across participant, items, or both. T. Curran and DL Hintzman (1995) demonstrated how biases from aggregation may lead to artifactual support for the model. The authors develop a hierarchical process-dissociation model that does not require aggregation for analysis. Most importantly, the Curran and Hintzman critique does not hold for this model. Model analysis provides for support of process dissociation—selective influence holds, and there is a dissociation in correlation patterns among participants and items. Items that are better recollected also elicit higher automatic activation. There is no correlation, however, across participants; that is, participants with higher recollection have no increased tendency toward automatic activation. The critique of aggregation is not limited to process dissociation. Aggregation distorts analysis in many nonlinear models, including signal detection, multinomial processing tree models, and strength models. Hierarchical modeling serves as a general solution for accurately fitting these psychological-processing models to data.