Department of Psychology, University of California, Berkeley.
Cogn Sci. 2018 Nov;42(8):2648-2669. doi: 10.1111/cogs.12670. Epub 2018 Sep 3.
Decades of psychological research have been aimed at modeling how people learn features and categories. The empirical validation of these theories is often based on artificial stimuli with simple representations. Recently, deep neural networks have reached or surpassed human accuracy on tasks such as identifying objects in natural images. These networks learn representations of real-world stimuli that can potentially be leveraged to capture psychological representations. We find that state-of-the-art object classification networks provide surprisingly accurate predictions of human similarity judgments for natural images, but they fail to capture some of the structure represented by people. We show that a simple transformation that corrects these discrepancies can be obtained through convex optimization. We use the resulting representations to predict the difficulty of learning novel categories of natural images. Our results extend the scope of psychological experiments and computational modeling by enabling tractable use of large natural stimulus sets.
几十年来，心理学研究一直致力于对人类学习特征和类别的方式进行建模。这些理论的实证验证通常基于具有简单表示的人为刺激。最近，深度神经网络在识别自然图像中的物体等任务上已经达到或超过了人类的准确性。这些网络学习了现实世界刺激的表示，这些表示可能会被利用来捕捉心理表示。我们发现，最先进的对象分类网络可以对自然图像的人类相似性判断进行惊人准确的预测，但它们无法捕捉到人类所代表的某些结构。我们表明，通过凸优化可以获得一种简单的转换来纠正这些差异。我们使用得到的表示来预测学习新的自然图像类别的难度。我们的结果通过使大型自然刺激集能够进行可处理的使用，扩展了心理实验和计算建模的范围。
