Hierarchical Bayesian measurement models for continuous reproduction of visual features from working memory
Hierarchical Bayesian measurement models for continuous reproduction of visual features from working memory
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DOI：
10.1167/17.5.11
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发表时间：
2017-05-01
影响因子：
1.8
通讯作者：
Lin, Hsuan-Yu
中科院分区：
文献类型：
--
作者：
Oberauer, Klaus;Stoneking, Colin;Lin, Hsuan-Yu
本文提出了两种视觉工作记忆测量模型的贝叶斯分层建模框架。该模型可以应用于圆形特征维上的响应分布，如连续再现（即延迟估计）任务所获得的。第一种测量模型是混合模型，它将响应分布描述为一种（Zhang & Luck, 2008）或几种（Bays, Catalao, & Husain, 2009）非米塞斯分布和均匀分布的混合。第二个模型是一种新颖的、基于干涉的测量模型。我们提出了两种模型的参数恢复模拟，证明了当少量试验由大量受试者补偿时，分层框架能够实现精确的参数估计。混合模型的仿真表明，贝叶斯层次框架在低性能条件下最小化了先前观察到的记忆精度估计偏差。干涉测量模型也可以得到无偏和合理精确的参数估计，但该模型的某些参数需要相对大量的数据才能进行精确测量。两种模型分别应用于两个实验数据集。实验1测量了内存集大小对模型参数的影响。实验2证明了干扰模型的假设，即目标特征在作为检索线索的维度上倾向于与接近目标的非目标特征混淆。
The article presents Bayesian hierarchical modeling frameworks for two measurement models for visual working memory. The models can be applied to the distributions of responses on a circular feature dimension, as obtained with the continuous reproduction (a. k. a. delayed estimation) task. The first measurement model is a mixture model that describes the response distributions as a mixture of one (Zhang & Luck, 2008) or several (Bays, Catalao, & Husain, 2009) von-Mises distribution(s) and a uniform distribution. The second model is a novel, interference-based measurement model. We present parameter recovery simulations for both models, demonstrating that the hierarchical framework enables precise parameter estimates when a small number of trials are compensated by a large number of subjects. Simulations with the mixture model show that the Bayesian hierarchical framework minimizes the previously observed estimation bias for memory precision in conditions of low performance. Unbiased and reasonably precise parameter estimates can also be obtained from the interference measurement model, though some parameters of this model demand a relatively large amount of data for precise measurement. Both models are applied to two experimental data sets. Experiment 1 measures the effect of memory set size on the model parameters. Experiment 2 provides evidence for the assumption in the interference model that the target feature tends to be confused with features of those nontargets that are close to the target on the dimension used as retrieval cue.