Department of Psychology, School of Social Sciences, University of Mannheim, L13, 15, 68161, Mannheim, Germany.
Behav Res Methods. 2023 Apr;55(3):981-996. doi: 10.3758/s13428-021-01769-1. Epub 2022 May 9.
Remembering an experienced event in a coherent manner requires the binding of the event's constituent elements. Such binding effects manifest as a stochastic dependency of the retrieval of event elements. Several approaches for modeling these dependencies have been proposed. We compare the contingency-based approach by Horner & Burgess (Journal of Experimental Psychology: General, 142(4), 1370-1383, 2013), related approaches using Yule's Q (Yule, Journal of the Royal Statistical Society, 75(6), 579-652, 1912) or an adjusted Yule's Q (c.f. Horner & Burgess, Current Biology, 24(9), 988-992, 2014), an approach based on item response theory (IRT, Schreiner et al., in press), and a nonparametric variant of the IRT-based approach. We present evidence from a simulation study comparing the five approaches regarding their empirical detection rates and susceptibility to different levels of memory performance, and from an empirical application. We found the IRT-based approach and its nonparametric variant to yield the highest power for detecting dependencies or differences in dependency between conditions. However, the nonparametric variant yielded increasing Type I error rates with increasing dependency in the data when testing for differences in dependency. We found the approaches based on Yule's Q to yield biased estimates and to be strongly affected by memory performance. The other measures were unbiased given no dependency or differences in dependency but were also affected by memory performance if there was dependency in the data or if there were differences in dependency, but to a smaller extent. The results suggest that the IRT-based approach is best suited for measuring binding effects. Further considerations when deciding for a modeling approach are discussed.
以连贯的方式记住一个有经验的事件需要将事件的组成元素绑定在一起。这种绑定效应表现为事件元素检索的随机依赖性。已经提出了几种用于建模这些依赖性的方法。我们比较了霍纳和伯吉斯（《实验心理学杂志：综合》，142（4），1370-1383，2013）的基于关联的方法，使用尤尔 Q（尤尔，《皇家统计学会杂志》，75（6），579-652，1912）或调整后的尤尔 Q（霍纳和伯吉斯，《当代生物学》，24（9），988-992，2014）的相关方法，基于项目反应理论（IRT，施赖纳等人，即将出版）的方法，以及 IRT 方法的非参数变体。我们从模拟研究中提供了证据，该研究比较了这五种方法在检测率和对不同记忆表现水平的敏感性方面的差异，并从实证应用中提供了证据。我们发现，基于 IRT 的方法及其非参数变体在检测条件之间的依赖性或依赖性差异方面具有最高的功效。然而，当测试依赖性差异时，非参数变体随着数据中依赖性的增加而产生越来越高的 I 型错误率。我们发现基于尤尔 Q 的方法产生有偏差的估计值，并受到记忆表现的强烈影响。在没有依赖性或依赖性差异的情况下，其他方法是无偏差的，但如果数据中有依赖性或依赖性存在差异，则会受到影响，但程度较小。结果表明，基于 IRT 的方法最适合测量绑定效应。在决定建模方法时还讨论了其他考虑因素。
