Likelihood-free Bayesian analysis of memory models.
Likelihood-free Bayesian analysis of memory models.
复制标题
记忆模型的无似然贝叶斯分析。
DOI：
10.1037/a0032458
复制
发表时间：
2013
影响因子：
5.4
通讯作者：
VanZandt,Trisha
中科院分区：
文献类型：
--
作者：
Turner,BrandonM;Dennis,Simon;VanZandt,Trisha
许多有影响力的记忆模型都是计算性的，因为它们的预测是通过模拟得出的。这意味着很难或不可能写出一个概率分布或似然性，将数据的随机行为表征为模型参数的函数。反过来，缺乏可能性意味着这些模型无法使用传统技术直接拟合数据。特别是，标准的贝叶斯分析这样的模型是不可能的。在这篇文章中，我们将研究如何使用一种称为近似贝叶斯计算（ABC）的新程序，这是一种规避可能性评估的贝叶斯分析方法，可以用于将计算模型拟合到内存数据。特别地，我们研究了情景记忆的线索决定模型（Dennis & Humphreys，2001）和有效提取模型（Shiffrin & Steyvers，1997）。我们将每个模型的分层版本拟合到Dennis、Lee和金内尔（2008）以及金内尔和Dennis（2012）的数据中。ABC分析允许我们探索每个模型中参数之间的关系，并评估它们与以前不可能的数据分析的相对拟合。（PsycINFO数据库记录（c）2019阿帕，保留所有权利）
Many influential memory models are computational in the sense that their predictions are derived through simulation. This means that it is difficult or impossible to write down a probability distribution or likelihood that characterizes the random behavior of the data as a function of the model’s parameters. In turn, the lack of a likelihood means that these models cannot be directly fitted to data using traditional techniques. In particular, standard Bayesian analyses of such models are impossible. In this article, we examine how a new procedure called approximate Bayesian computation (ABC), a method for Bayesian analysis that circumvents the evaluation of the likelihood, can be used to fit computational models to memory data. In particular, we investigate the bind cue decide model of episodic memory (Dennis & Humphreys, 2001) and the retrieving effectively from memory model (Shiffrin & Steyvers, 1997). We fit hierarchical versions of each model to the data of Dennis, Lee, and Kinnell (2008) and Kinnell and Dennis (2012). The ABC analysis permits us to explore the relationships between the parameters in each model as well as evaluate their relative fits to data—analyses that were not previously possible.(PsycINFO Database Record (c) 2019 APA, all rights reserved)