People are remarkably good at remembering photographs. To further investigate the nature of the stored representations and the fidelity of human memories, it would be useful to evaluate the visual similarity of stimuli presented in experiments. Here, we explored the possible use of convolutional neural networks (CNN) as a measure of perceptual or representational similarity of visual scenes with respect to visual memory research. In Experiment 1, we presented participants with sets of nine images from the same scene category and tested whether they were able to detect the most distant scene in the image space defined by CNN. Experiment 2 was a visual variant of the Deese-Roediger-McDermott paradigm. We asked participants to remember a set of photographs from the same scene category. The photographs were preselected based on their distance to a particular visual prototype (defined as centroid of the image space). In the recognition test, we observed higher false alarm rates for scenes closer to this visual prototype. Our findings show that the similarity measured by CNN is reflected in human behavior: people can detect odd-one-out scenes or be lured to false alarms with similar stimuli. This method can be used for further studies regarding visual memory for complex scenes.

人们非常善于记住照片。为了进一步研究存储表征的性质和人类记忆的保真度，评估实验中呈现的刺激的视觉相似性将是有用的。在这里，我们探讨了卷积神经网络 (CNN) 作为衡量视觉场景在视觉记忆研究方面的感知或表征相似性的可能用途。在实验 1 中，我们向参与者展示了一组来自同一场景类别的 9 张图像，并测试他们是否能够检测到 CNN 定义的图像空间中最远的场景。实验 2 是 Deese-Roediger-McDermott 范式的视觉变体。我们要求参与者记住一组来自同一场景类别的照片。这些照片是根据它们与特定视觉原型（定义为图像空间的质心）的距离来预选的。在识别测试中，我们观察到更接近这个视觉原型的场景的误报率更高。我们的研究结果表明，CNN 测量的相似性反映在人类行为中：人们可以检测到奇一场景或被类似刺激引诱到误报。该方法可用于进一步研究复杂场景的视觉记忆。