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首页> 外文期刊>Cognition: International Journal of Cognitive Psychology >Hierarchical organization in visual working memory: From global ensemble to individual object structure
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Hierarchical organization in visual working memory: From global ensemble to individual object structure

机译:Visual工作内存中的分层组织:从全局集合到各个对象结构

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When remembering a natural scene, both detailed information about specific objects and summary representations such as the gist of a scene are encoded. However, formal models of change detection that are used to estimate working memory capacity, typically assume observers simply encode and maintain memory representations that are treated independently from one another without considering the (hierarchical) object or scene structure. To overcome this limitation, we present a hierarchical variant of the change detection task that attempts to formalize the role of object structure, thus, allowing for richer, more graded memory representations. We demonstrate that detection of a global-object change precedes local-object changes of hierarchical shapes to a large extent. Moreover, when systematically varying object repetitions between individual items at a global or a local level, memory performance declines mainly for repeated global objects, but not for repeated local objects, which suggests that ensemble (i.e., summary) representations are likewise biased toward a global level. In addition, this global memory precedence effect is shown to be independent from encoding durations, and mostly cannot be attributed to differences in saliency or shape discriminability at global/local object levels. This pattern of results is suggestive of a global/local difference occurring primarily during memory maintenance. Altogether, these findings challenge visual-working-memory (vWM) models that propose that a fixed number of objects can be remembered regardless of the individual object structure. Instead, our results support a hierarchical model that emphasizes the role for structured representations among objects in vWM. (C) 2016 Elsevier B.V. All rights reserved.
机译:记住自然场景时,编码有关特定对象和摘要表示的详细信息,例如场景的主旨。然而,用于估计工作存储器容量的改变检测的正式模型通常假设观察者只是在不考虑(分层)对象或场景结构的情况下独立地对待和维护彼此对待的存储器表示。为了克服这种限制,我们介绍了改变检测任务的分层变体,该任务试图模拟对象结构的角色,从而允许更丰富,更级别的存储器表示。我们演示了全局对象改变的检测在很大程度上之前的本地对象变化。此外,当在全局或本地级别之间系统地改变各个项目之间的对象重复时,内存性能主要针对重复的全局对象的拒绝,但不适用于重复的本地对象,这表明集合(即,摘要)表示同样偏置为全局偏置等级。此外,该全局内存优先效应显示为独立于编码持续时间,并且大多数不能归因于全局/局部对象级别的显着性或形状辨别性的差异。这种结果模式提示了主要在内存维护期间发生的全局/局部差异。完全是,这些发现挑战了视觉工作记忆(VWM)模型,该模型提出了可以记住固定数量的对象,而不管各个对象结构。相反,我们的结果支持分层模型,它强调了VWM中对象之间结构化表示的作用。 (c)2016年Elsevier B.v.保留所有权利。

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