Signal Detection Models with Random Participant and Item Effects
Signal Detection Models with Random Participant and Item Effects
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具有随机参与者和项目效应的信号检测模型
DOI：
10.1007/s11336-005-1350-6
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发表时间：
2007
期刊：
影响因子：
3
通讯作者：
M. Naveh
中科院分区：
文献类型：
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作者：
Jeffrey N. Rouder;Jun Lu;Dongchu Sun;P. Speckman;R. Morey;M. Naveh
信号检测理论为再认记忆范式中记忆能力的测量提供了方便。在这些范例中，随机选择的参与者被要求研究随机选择的项目。在实践中，研究人员汇总了项目或参与者或两者的数据。信号检测模型是非线性的;因此，对聚合数据的分析是不一致的。事实上，记忆能力被低估了，即使是在大样本的限制下。我们提出了两个层次贝叶斯模型，同时考虑到参与者和项目的变异性。我们展示了这些模型如何准确估计参与者的记忆能力以及项目的记忆能力。该模型的基准与模拟研究，并应用到一个新的数据集。
The theory of signal detection is convenient for measuring mnemonic ability in recognition memory paradigms. In these paradigms, randomly selected participants are asked to study randomly selected items. In practice, researchers aggregate data across items or participants or both. The signal detection model is nonlinear; consequently, analysis with aggregated data is not consistent. In fact, mnemonic ability is underestimated, even in the large-sample limit. We present two hierarchical Bayesian models that simultaneously account for participant and item variability. We show how these models provide for accurate estimation of participants’ mnemonic ability as well as the memorability of items. The model is benchmarked with a simulation study and applied to a novel data set.
DOI：
10.1037/0096-3445.137.2.370
发表时间：
2008
期刊：
Journal of experimental psychology. General
影响因子：
--
作者：
Rouder,JeffreyN;Lu,Jun;Morey,RichardD;Sun,Dongchu;Speckman,PaulL
通讯作者：
Speckman,PaulL