视觉工作记忆非常有限,它的能力与许多认知功能指标有关。出于这个原因,人们对了解其体系结构及其有限容量的来源非常感兴趣。作为这项研究工作的一部分,研究人员经常试图将视觉工作记忆错误分解为不同类型的错误,不同的起源。最常见的一种内存错误称为“交换”,\“其中人们报告的值与未被探测的项目非常相似(例如,一个不正确的,非目标项目)。这通常被认为反映了混乱,像位置绑定错误,这导致报告错误的项目。可靠且有效地捕获交换速率非常重要,因为它允许研究人员准确地分解不同的内存错误来源并阐明引起它们的过程。这里,我们问不同的视觉工作记忆模型是否会产生稳健和一致的掉期率估计。这是文献中的一个主要差距,因为在实证和建模工作中,研究人员在不激励他们选择互换模型的情况下测量互换。因此,我们使用广泛的参数恢复模拟与三个主流的互换模型,以证明如何选择测量模型可以导致非常大的差异估计的互换利率。我们发现,这些选择可能会对估计掉期利率在不同条件下的变化产生重大影响。特别是,我们考虑的三个模型中的每一个都可以导致对数据的不同的定量和定性解释。我们的工作是对研究人员的警示,也是基于模型的视觉工作记忆过程测量的指南。
Visual working memory is highly limited, and its capacity is tied to many indices of cognitive function. For this reason, there is much interest in understanding its architecture and the sources of its limited capacity. As part of this research effort, researchers often attempt to decompose visual working memory errors into different kinds of errors, with different origins. One of the most common kinds of memory error is referred to as a \"
swap,\" where people report a value that closely resembles an item that was not probed (e.g., an incorrect, non-target item). This is typically assumed to reflect confusions, like location binding errors, which result in the wrong item being reported. Capturing
swap rates reliably and validly is of great importance because it permits researchers to accurately decompose different sources of memory errors and elucidate the processes that give rise to them. Here, we ask whether different visual working memory models yield robust and consistent estimates of
swap rates. This is a major gap in the literature because in both empirical and modeling work, researchers measure swaps without motivating their choice of swap model. Therefore, we use extensive parameter recovery simulations with three mainstream swap models to demonstrate how the choice of measurement model can result in very large differences in estimated
swap rates. We find that these choices can have major implications for how
swap rates are estimated to change across conditions. In particular, each of the three models we consider can lead to differential quantitative and qualitative interpretations of the data. Our work serves as a cautionary note to researchers as well as a guide for model-based measurement of visual working memory processes.