ISSN 0439-755X
CN 11-1911/B

心理学报 ›› 2015, Vol. 47 ›› Issue (7): 851-858.doi: 10.3724/SP.J.1041.2015.00851

• 论文 • 上一篇    下一篇



  1. (辽宁师范大学脑与认知神经科学研究中心, 大连 116029)
  • 收稿日期:2014-10-09 发布日期:2015-07-25 出版日期:2015-07-25
  • 通讯作者: 刘强, E-mail:
  • 基金资助:


Memory Mechanism of Feature Binding in Visual Working Memory

XUE ChengBo; YE ChaoXiong; ZHANG Yin; LIU Qiang   

  1. (Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian 116029, China)
  • Received:2014-10-09 Online:2015-07-25 Published:2015-07-25
  • Contact: LIU Qiang, E-mail:


弱客体理论认为, 视觉工作记忆系统是由众多特征存储的子系统组成, 不同纬度的特征独立存储在相应的有限容量的子系统中, 相互之间不会竞争记忆资源。而对于特征之间绑定关系的记忆存在不同的观点。一种观点认为绑定关系的记忆需要注意维持, 因此绑定关系的记忆会占用到特征记忆的资源; 另一种观点认为绑定关系的记忆是自动发生的, 不需要注意的维持。本实验的目的是探究特征绑定关系的记忆是否是自动发生的。在实验中, 我们设计了两种任务, 一种任务是只记忆颜色, 另一种任务是记忆颜色跟位置及其之间的绑定关系, 并测试分析视觉工作记忆相关的ERP成分CDA。结果发现两种任务条件下的CDA波幅之间没有显著差异, 说明视觉工作记忆中的特征绑定是自动发生的。

关键词: 视觉工作记忆, 弱客体理论, 特征绑定, 注意, CDA


A weak object-based theory considers visual working memory to be made up of many subsystems. Rather than competing for memory resources, this theory suggests that information about different feature dimensions is stored in independent subsystems that each has a limited capacity of memory resource.In addition to describing the storage capacity limitations of different feature dimensions, supporters of weak object-based theory also argue that the binding between features can itself be a dimension of information to be stored. There remains controversy concerning whether the storage of binding information is processed automatically and whether it needs attentional resources. Treisman et al. (2002) suggested that binding in visual memory might require attention to be focused on maintaining the links between features during the delay. Similarly feature-integration theory proposes that, when multiple objects are present, focused attention is required to correctly bind features for initial perception. In contrast, Allen et al. (2006) by adding a digit-span task, explored whether feature binding required additional resources. They found that memory for bound conditions did not require more attention than memory for single feature conditions, and suggested that binding in itself did not require attentional resources. To examine whether or not binding needs attentional resources we measured participants’ ERPs in two conditions. In the single feature condition participants were required to judge whether the color of objects had changed from the initial display. On change trials, the objects occupied the same position but one item had changed to a new color that had not appeared in the initial display.In the feature binding condition, on no-change trials, participants had to judge both whether the color and the location in the test display had changed from the initial display. For change trials, the colors of any two objects exchanged position so that the relationship between colorand location changed for two objects. We compared the amplitude differences between the two conditions in the Contralateral Delay Activity (CDA) component of ERP data, to examine whether feature binding required attention. Accuracy for each condition was greater than 75% and there wereno significant behavioral differences between the single feature condition and the binding condition.Importantly, the ERP resultsalso showedno significantdifferences inamplitude across the two conditions. There was a significant main effect of set size. There were equal amplitudes for 3 items and 4 items, but amplitudes were significantly larger for both 3 and 4 item than for 2 items. At the same time, there was no significant interaction between condition and number of items. All of the results support the hypothesis that no additional memory resources are required to store the binding between two features (color and position of an item) compared to those required to store a single feature (color). These results confirm that the binding relationship needs no attention resources. We take the result to be evidence of a relatively automatic visual feature binding mechanism in working memory.

Key words: visual working memory, weak object-based theory, feature binding, attention, CDA