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首页> 外文期刊>Journal of vision >Structured representations in visual working memory: Using results from individual displays to constrain cognitive theory
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Structured representations in visual working memory: Using results from individual displays to constrain cognitive theory

机译:视觉工作记忆中的结构化表示:使用单个显示的结果来约束认知理论

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Influential models of visual working memory assume we encode items independently and examine how many individual items can be remembered. However, observers remember not only individual items but also ensemble statistics, like mean size or color.Here we show that these levels of representation are not independent, even when observers remember displays of random colors. Exp. 1:Observers remembered several colored dots, and after a delay, reported the color of a dot selected at random. We estimated the rate of random guessing, and whether it depends on how items are clustered in color space. At set size 3, even subtle differences in clustering resulted in different guess rates: with distractors 90 and 180?° in color space from the tested item, the guess rate was 24%; at 60/120?°, the guess rate was only 13% (p0.05). In fact, with distractors somewhat clustered, observers rarely guess even at set size 5 (distractors: 30/60/90/120?°; guesses: 7%). These clustering effects suggest that observers make use of the ensemble statistics to know something about all of the items. Importantly, clustering in color space is inherently more likely at low set sizes, and thus guess rates at low set sizes are systematically underestimated in all experiments using these methods (up to 30%). Exp. 2:In a second experiment, we tested hundreds of observers on the same displays (N=465 on each), and found that models that do not take into account the relationship between items fail to fit the data on any particular display, even though they fit on average across all displays. Models that take into account how clustered in hue the items are (e.g., ensembles) are required. We suggest that observersa?? use of ensembles, which vary from display-to-display, make studying capacity by averaging across displays impossible, and requires a new framework based on large amounts of data on individual displays.
机译:视觉工作记忆的有影响力的模型假设我们独立地编码项目,并检查可以记住多少个单独的项目。但是,观察者不仅记住单个项目,而且还记住诸如平均大小或颜色之类的整体统计数据。在这里,我们证明了即使当观察者记住随机颜色的显示时,这些表示形式也不是独立的。经验1:观察者记得几个彩色的点,并在延迟后报告了随机选择的点的颜色。我们估计了随机猜测的速度,以及它是否取决于颜色空间中项目的聚类方式。在大小为3的情况下,即使是细微的聚类差异也会导致不同的猜测率:在被测项目的颜色空间中有90和180?°的干扰物时,猜测率为24%;在60/120°°时,猜测率仅为13%(p <0.05)。实际上,由于干扰因素有些聚集,观察者甚至很少在设定大小5时猜测(干扰因素:30/60/90/120°;猜测:7%)。这些聚类效果表明,观察者可以利用集合统计信息来了解所有项目。重要的是,在低设置尺寸下,色彩空间中的聚类本来就更有可能,因此,在所有使用这些方法的实验中,低设置尺寸下的猜测率都被系统低估了(最多30%)。经验2:在第二个实验中,我们在相同的显示器上测试了数百名观察者(每个显示器上的N = 465),发现没有考虑项目之间的关系的模型无法将数据拟合到任何特定的显示器上,即使它们平均适合所有显示器。需要考虑到项目在色调上如何聚类(例如,合奏)的模型。我们建议观察者?集成的使用因显示器而异,因此无法通过平均各个显示器来研究容量,并且需要基于单个显示器上大量数据的新框架。

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