Visual short-term memory (VSTM) is a cognitive structure that temporarily maintains a limited amount of visual information in the service of current cognitive goals. There is active theoretical debate regarding how limits in VSTM should be construed. According to discrete-slot models of capacity, these limits are set in terms of a discrete number of slots that store individual objects in an all-or-none fashion. According to alternative continuous resource models, the limits of VSTM are set in terms of a resource that can be distributed to bolster some representations over others in a graded fashion. Hybrid models have also been proposed. We tackled the classic question of how to construe VSTM structure in a novel way, by examining how contending models explain data within traditional VSTM tasks and also how they generalize across different VSTM tasks. Specifically, we fit theoretical ROCs derived from a suite of models to two popular VSTM tasks: a change detection task in which participants had to remember simple features and a rapid serial visual presentation task in which participants had to remember real-world objects. In 3 experiments we assessed the fit and predictive ability of each model and found consistent support for pure resource models of VSTM. To gain a fuller understanding of the nature of limits in VSTM, we also evaluated the ability of these models to jointly model the two tasks. These joint modeling analyses revealed additional support for pure continuous-resource models, but also evidence that performance across the two tasks cannot be captured by a common set of parameters. We provide an interpretation of these signal detection models that align with the idea that differences among memoranda and across encoding conditions alter the memory signal of representations in VSTM.

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容量有K吗？评估视觉短期记忆的结构

视觉短期记忆 (VSTM) 是一种认知结构，可暂时保持有限数量的视觉信息，以服务于当前的认知目标。关于如何解释 VSTM 中的限制，存在活跃的理论争论。根据容量的离散插槽模型，这些限制是根据以全有或无方式存储单个对象的插槽的离散数量设置的。根据替代的连续资源模型，VSTM 的限制是根据可以分布的资源设置的，以分级方式支持某些表示。还提出了混合模型。我们解决了如何以新颖的方式解释 VSTM 结构的经典问题，通过检查竞争模型如何解释传统 VSTM 任务中的数据，以及它们如何在不同的 VSTM 任务中泛化。具体来说，我们将从一套模型中得出的理论 ROC 拟合到两个流行的 VSTM 任务中：参与者必须记住简单特征的变化检测任务和参与者必须记住现实世界对象的快速串行视觉呈现任务。在 3 个实验中，我们评估了每个模型的拟合和预测能力，并发现对 VSTM 纯资源模型的一致支持。为了更全面地了解 VSTM 中限制的性质，我们还评估了这些模型联合建模这两个任务的能力。这些联合建模分析揭示了对纯连续资源模型的额外支持，但也有证据表明，两个任务的性能无法通过一组通用参数来捕获。我们提供了对这些信号检测模型的解释，这些模型与备忘录之间和不同编码条件之间的差异会改变 VSTM 中表示的记忆信号的想法一致。