关于识别变化检测任务模型以测量视觉工作记忆中的存储容量和注意力的说明。
A note on the identification of change detection task models to measure storage capacity and attention in visual working memory.
机构信息
LMF, University of California-Berkeley, Berkeley, CA, USA.
SJL, Department of Psychology, University of California-Davis, Davis, CA, USA.
出版信息
Behav Res Methods. 2019 Jun;51(3):1360-1370. doi: 10.3758/s13428-018-1082-z.
Abstract
The change detection task is a common method for assessing the storage capacity of working memory, but estimates of memory capacity from this task can be distorted by lapses of attention. When combined with appropriate mathematical models, some versions of the change detection task make it possible to separately estimate working memory and the probability of attentional lapses. In principle, these models should allow researchers to isolate the effects of experimental manipulations, group differences, and individual differences on working memory capacity and on the rate of attentional lapses. However, the present research found that two variants of a widely accepted model of the change detection task are not mathematically identified.
摘要
变化检测任务是评估工作记忆存储容量的常用方法，但该任务的记忆容量估计可能会受到注意力不集中的影响。当与适当的数学模型结合使用时，某些版本的变化检测任务可以分别估计工作记忆和注意力不集中的概率。原则上，这些模型应该允许研究人员隔离实验操作、群体差异和个体差异对工作记忆容量和注意力不集中率的影响。然而，本研究发现，一个广泛接受的变化检测任务模型的两个变体在数学上无法识别。
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