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Evidence of gradual loss of precision for simple features and complex objects in visual working memory

机译:视觉工作记忆中简单特征和复杂对象的精度逐渐下降的证据

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摘要

Previous studies have suggested that people can maintain prioritized items in visual working memory for many seconds, with negligible loss of information over time. Such findings imply that working memory representations are robust to the potential contaminating effects of internal noise. However, once visual information is encoded into working memory, one might expect it to inevitably begin degrading over time, as this actively maintained information is no longer tethered to the original perceptual input. Here, we examined this issue by evaluating working memory for single central presentations of an oriented grating, color patch, or face stimulus, across a range of delay periods (1, 3, 6 or 12s). We applied a mixture-model analysis to distinguish changes in memory precision over time from changes in the frequency of outlier responses that resemble random guesses. For all three types of stimuli, participants exhibited a clear and consistent decline in the precision of working memory as a function of temporal delay, as well as a modest increase in guessing-related responses for colored patches and face stimuli. We observed a similar loss of precision over time while controlling for temporal distinctiveness. Our results demonstrate that visual working memory is far from lossless: while basic visual features and complex objects can be maintained in a quite stable manner over time, these representations are still subject to noise accumulation and complete termination.
机译:先前的研究表明,人们可以在视觉工作记忆中保留优先项目几秒钟,而随着时间的流逝,信息的损失可以忽略不计。这些发现暗示工作记忆表示对于内部噪声的潜在污染影响具有鲁棒性。然而,一旦视觉信息被编码到工作存储器中,人们可能会期望它随着时间的推移不可避免地开始退化,因为这种主动维护的信息不再被束缚在原始的感知输入上。在这里,我们通过评估在一定延迟时间范围(1、3、6或12s)内定向光栅,色块或面部刺激的单个中心表示的工作记忆来研究此问题。我们应用了混合模型分析,以区分随时间变化的内存精度变化与类似于随机猜测的异常响应频率的变化。对于所有这三种类型的刺激,参与者表现出的工作记忆的精确度和持续性的下降是时间延迟的函数,并且对有色斑块和面部刺激的猜测相关反应也有适度的增加。我们观察到随着时间的流逝,控制时间的独特性的同时,精度也随着时间的流逝而下降。我们的结果表明,视觉工作记忆远非无损:尽管基本的视觉特征和复杂的对象可以随着时间的推移以相当稳定的方式进行维护,但是这些表示仍然会受到噪声累积和完全终止的影响。

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