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Visual Long-term Memory Has A Massive Storage Capacity For Object Details

机译:视觉长期内存具有海量的对象详细信息存储容量

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摘要

One of the major lessons of memory research has been that human memory is fallible, imprecise, and subject to interference. Thus, although observers can remember thousands of images, it is widely assumed that these memories lack detail. Contrary to this assumption, here we show that long-term memory is capable of storing a massive number of objects with details from the image. Participants viewed pictures of 2,500 objects over the course of 5.5 h. Afterward, they were shown pairs of images and indicated which of the two they had seen. The previously viewed item could be paired with either an object from a novel category, an object of the same basic-level category, or the same object in a different state or pose. Performance in each of these conditions was remarkably high (92%, 88%, and 87%, respectively), suggesting that participants successfully maintained detailed representations of thousands of images. These results have implications for cognitive models, in which capacity limitations impose a primary computational constraint (e.g., models of object recognition), and pose a challenge to neural models of memory storage and retrieval, which must be able to account for such a large and detailed storage capacity.
机译:记忆研究的主要教训之一是人类记忆是易犯错误,不精确且容易受到干扰的。因此,尽管观察者可以记住成千上万个图像,但是人们普遍认为这些记忆缺乏细节。与此假设相反,这里我们表明长期内存能够存储大量带有图像细节的对象。参与者在5.5小时内观看了2500个物体的照片。之后,为他们显示了成对的图像,并指出了他们看到的两个图像中的哪个。先前查看的项目可以与来自新颖类别的对象,具有相同基本级别类别的对象或处于不同状态或姿势的相同对象配对。在每种情况下的性能都非常高(分别为92%,88%和87%),这表明参与者成功地维护了数千张图像的详细表示。这些结果对认知模型具有重要意义,在认知模型中,容量限制施加了主要的计算约束(例如,对象识别模型),并且对内存存储和检索的神经模型提出了挑战,因为神经模型必须能够解决如此大的需求。详细的存储容量。

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