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在大集合大小下记忆分辨率的平稳状态表明视觉工作记忆中存在离散的资源限制。

The plateau in mnemonic resolution across large set sizes indicates discrete resource limits in visual working memory.

作者信息

Anderson David E, Awh Edward

机构信息

Department of Psychology, 1227 University of Oregon, Eugene, OR 97403, USA.

出版信息

Atten Percept Psychophys. 2012 Jul;74(5):891-910. doi: 10.3758/s13414-012-0292-1.

Abstract

The precision of visual working memory (WM) representations declines monotonically with increasing storage load. Two distinct models of WM capacity predict different shapes for this precision-by-set-size function. Flexible-resource models, which assert a continuous allocation of resources across an unlimited number of items, predict a monotonic decline in precision across a large range of set sizes. Conversely, discrete-resource models, which assert a relatively small item limit for WM storage, predict that precision will plateau once this item limit is exceeded. Recent work has demonstrated such a plateau in mnemonic precision. Moreover, the set size at which mnemonic precision reached asymptote has been strongly predicted by estimated item limits in WM. In the present work, we extend this evidence in three ways. First, we show that this empirical pattern generalizes beyond orientation memory to color memory. Second, we rule out encoding limits as the source of discrete limits by demonstrating equivalent performance across simultaneous and sequential presentations of the memoranda. Finally, we demonstrate that the analytic approach commonly used to estimate precision yields flawed parameter estimates when the range of stimulus space is narrowed (e.g., a 180º rather than a 360º orientation space) and typical numbers of observations are collected. Such errors in parameter estimation reconcile an apparent conflict between our findings and others based on different stimuli. These findings provide further support for discrete-resource models of WM capacity.

摘要

视觉工作记忆(WM)表征的精度会随着存储负荷的增加而单调下降。两种不同的WM容量模型预测了这种精度随集合大小变化的函数的不同形状。灵活资源模型主张在无限数量的项目上连续分配资源,预测在大范围的集合大小上精度会单调下降。相反,离散资源模型主张WM存储存在相对较小的项目限制,预测一旦超过这个项目限制,精度将趋于平稳。最近的研究已经证明了记忆精度存在这样一个平稳阶段。此外,记忆精度达到渐近线时的集合大小已经被WM中估计的项目限制强烈预测。在本研究中,我们从三个方面扩展了这一证据。首先,我们表明这种经验模式不仅适用于方向记忆,也适用于颜色记忆。其次,我们通过证明在同时呈现和顺序呈现记忆材料时具有等效的表现,排除了编码限制作为离散限制的来源。最后,我们证明当刺激空间范围变窄(例如,180°而不是360°的方向空间)且收集典型数量的观察数据时,通常用于估计精度的分析方法会产生有缺陷的参数估计。参数估计中的这些误差调和了我们的发现与基于不同刺激的其他发现之间的明显冲突。这些发现为WM容量的离散资源模型提供了进一步的支持。

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