Emrich Stephen M, Lockhart Holly A, Al-Aidroos Naseem
Department of Psychology.
Department of Psychology, University of Guelph.
J Exp Psychol Hum Percept Perform. 2017 Jul;43(7):1454-1465. doi: 10.1037/xhp0000398. Epub 2017 Apr 3.
Though it is clear that it is impossible to store an unlimited amount of information in visual working memory (VWM), the limiting mechanisms remain elusive. While several models of VWM limitations exist, these typically characterize changes in performance as a function of the number of to-be-remembered items. Here, we examine whether changes in spatial attention could better account for VWM performance, independent of load. Across 2 experiments, performance was better predicted by the prioritization of memory items (i.e., attention) than by the number of items to be remembered (i.e., memory load). This relationship followed a power law, and held regardless of whether performance was assessed based on overall precision or any of 3 measures in a mixture model. Moreover, at large set sizes, even minimally attended items could receive a small proportion of resources, without any evidence for a discrete-capacity on the number of items that could be maintained in VWM. Finally, the observed data were best fit by a variable-precision model in which response error was related to the proportion of resources allocated to each item, consistent with a model of VWM in which performance is determined by the continuous allocation of attentional resources during encoding. (PsycINFO Database Record
虽然很明显,在视觉工作记忆(VWM)中存储无限量的信息是不可能的,但限制机制仍然难以捉摸。虽然存在几种VWM限制模型,但这些模型通常将性能变化描述为待记忆项目数量的函数。在这里,我们研究空间注意力的变化是否能更好地解释VWM性能,而不受负载的影响。在2个实验中,记忆项目的优先级(即注意力)比待记忆项目的数量(即记忆负载)能更好地预测性能。这种关系遵循幂律,并且无论性能是基于整体精度还是混合模型中的3种测量方法之一进行评估,都成立。此外,在大集合大小下,即使是最少被关注的项目也能获得一小部分资源,没有任何证据表明VWM中可维持的项目数量存在离散容量。最后,观察到的数据最适合一个可变精度模型,其中反应误差与分配给每个项目的资源比例相关,这与一个VWM模型一致,在该模型中,性能是由编码过程中注意力资源的连续分配决定的。(PsycINFO数据库记录 )