Investigations into the way that information is held and integrated within the visual system provides some basis for understanding how visual information is represented and processed. Just over sixty years ago, Swets, Shipley, McKey, and Green (1959) demonstrated that performance within an auditory detection task increases as a function of the square root of the number of stimulus observation intervals, following the predictions of basic sampling theory, indicating the efficient perceptual integration of stimulus information. This principle of observer performance contingent on a constant rate of stimulus sampling also forms the basis of the sample-size model (Palmer, 1990; Sewell, Lilburn, & Smith, 2014) which seeks to provide an account of how memory resources might be divided among item representations in visual short-term memory (VSTM). In this article, we combine the multiple observations paradigm of Swets and colleagues with the VSTM paradigm of Sewell and colleagues and show that the sample-size relationship accounts for both the increase in performance with the number of presentation intervals and the way that performance changes as a function of the number of items in memory. The model provides an account of both the overall information limit of VSTM and an account of the dynamics of that limit, demonstrating not only that observers can selectively update specific representations in memory but that performance in this task is accounted for by a simple statistical constraint. We discuss the implications for models of VSTM capacity and architecture generally, focusing on the implications for objecthood and the characteristics of encoding to and retrieval from memory.

中文翻译：

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单一、简单的统计机制解释了视觉短期记忆中的资源分布和时间更新

对信息在视觉系统中的保存和整合方式的研究为理解视觉信息的表示和处理方式提供了一些基础。就在 60 多年前，Swets、Shipley、McKey 和 Green（1959）证明了听觉检测任务中的性能随着刺激观察间隔数量的平方根的函数而增加，遵循基本抽样理论的预测，表明刺激信息的有效感知整合。这种取决于恒定刺激采样率的观察者表现原则也构成了样本量模型（Palmer，1990；Sewell、Lilburn 和 Smith，2014）的基础，该模型试图提供关于如何划分记忆资源的说明在视觉短期记忆 (VSTM) 中的项目表示中。在本文中，我们将 Swets 及其同事的多重观察范式与 Sewell 及其同事的 VSTM 范式结合起来，并表明样本大小关系既可以解释表现间隔数量的增加，也可以解释表现变化的方式内存中项目数的函数。该模型提供了 VSTM 的整体信息限制和该限制的动态说明，不仅证明观察者可以选择性地更新内存中的特定表示，而且该任务的性能由一个简单的统计约束来解释。我们一般讨论对 VSTM 容量和架构模型的影响，重点是对对象性的影响以及对记忆进行编码和检索的特征。