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首页> 外文期刊>Journal of vision >The Binding Pool model of VWM: A model for storing individuated objects in a shared resource pool
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The Binding Pool model of VWM: A model for storing individuated objects in a shared resource pool

机译：VWM的绑定池模型：一种用于在共享资源池中存储单个对象的模型

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Two prevalent models that describe Visual Working Memory (VWM) assume that information is either stored in discrete slots or within a shared resource pool. To develop the theoretical landscape further, we propose a hybrid model called the Binding Pool model. This model details how multiple items can be encoded and retrieved individually yet interact with one another in a distributed binding pool using a Type/Token architecture. These processes use simple neural mechanisms that can rapidly encode arbitrary connections between different features (types), a location, and an object-file (token). These connections are stored by accumulating and storing simulated neural activity in a set of neurons called the binding pool. The model provides a unified framework for understanding VWM capabilities as measured by change detection and continuous report tasks. The Binding Pool model also provides a mechanism for explaining simple ensemble effects, such as the shifting of a stored representation towards another (Huang & Sekular, 2010). This arises because tokens share representational space in the binding pool, creating crosstalk between two stored items. The Binding Pool model can also generate predictions, which simultaneously test the validity of the model and may help to drive further research. One prediction of the model that was recently confirmed is increased precision in a directed forgetting paradigm in which participants are instructed to forget a specific stimulus. In a forgetting trial, the precision of the remaining stimulus is higher relative to a non-forgetting trial, but this precision is still lower than precision of a representation in a single item trial. (Williams, Hong, Kang, Carlisle, & Woodman, 2013). In the model, reducing the activity of binding pool neurons connected to the forgotten item, reduces interference during retrieval, which enhances precision of the remaining items. The model also predicts that encoding more features per item reduces precision.
机译：描述视觉工作内存（VWM）的两个流行模型假定信息存储在离散插槽中或共享资源池中。为了进一步发展理论前景，我们提出了一种称为“绑定池”模型的混合模型。此模型详细说明了如何使用类型/令牌体系结构在分布式绑定池中单独编码和检索多个项目，以及如何相互交互。这些过程使用简单的神经机制，可以快速编码不同特征（类型），位置和对象文件（令牌）之间的任意连接。通过在一组称为绑定池的神经元中累积和存储模拟的神经活动来存储这些连接。该模型提供了一个统一的框架，用于了解通过变更检测和连续报告任务衡量的VWM功能。绑定池模型还提供了一种机制，用于解释简单的整体效果，例如，将存储的表示向另一个移动（Huang＆Sekular，2010）。之所以会出现这种情况，是因为令牌在绑定池中共享表示空间，从而在两个存储的项目之间产生了串扰。绑定池模型还可以生成预测，这些预测可以同时测试模型的有效性，并可能有助于推动进一步的研究。最近被确认的模型预测之一是在定向遗忘范例中提高了精度，在这种范例中，指示参与者忘记特定刺激。在遗忘试验中，相对于非遗忘试验，剩余刺激物的精度更高，但此精度仍低于单项试验中表示的精度。 （威廉姆斯，康宏，卡莱尔和伍德曼，2013年）。在该模型中，减少与被遗忘物品连接的结合池神经元的活性，减少检索过程中的干扰，从而提高了其余物品的精度。该模型还预测对每个项目编码更多特征会降低精度。

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