Department of Experimental Psychology, Justus-Liebig University Giessen, Giessen, Germany.
Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Giessen, Germany.
PLoS Comput Biol. 2021 Jun 1;17(6):e1008981. doi: 10.1371/journal.pcbi.1008981. eCollection 2021 Jun.
Shape is a defining feature of objects, and human observers can effortlessly compare shapes to determine how similar they are. Yet, to date, no image-computable model can predict how visually similar or different shapes appear. Such a model would be an invaluable tool for neuroscientists and could provide insights into computations underlying human shape perception. To address this need, we developed a model ('ShapeComp'), based on over 100 shape features (e.g., area, compactness, Fourier descriptors). When trained to capture the variance in a database of >25,000 animal silhouettes, ShapeComp accurately predicts human shape similarity judgments between pairs of shapes without fitting any parameters to human data. To test the model, we created carefully selected arrays of complex novel shapes using a Generative Adversarial Network trained on the animal silhouettes, which we presented to observers in a wide range of tasks. Our findings show that incorporating multiple ShapeComp dimensions facilitates the prediction of human shape similarity across a small number of shapes, and also captures much of the variance in the multiple arrangements of many shapes. ShapeComp outperforms both conventional pixel-based metrics and state-of-the-art convolutional neural networks, and can also be used to generate perceptually uniform stimulus sets, making it a powerful tool for investigating shape and object representations in the human brain.
形状是物体的一个决定性特征，人类观察者可以毫不费力地比较形状来确定它们的相似程度。然而，迄今为止，没有任何可用于图像的模型可以预测形状的视觉相似性或差异性。这样的模型将是神经科学家的宝贵工具，并能深入了解人类形状感知的计算基础。为了满足这一需求，我们开发了一种基于超过 100 个形状特征的模型（例如，面积、紧凑度、傅里叶描述符）。当我们用超过 25000 个动物轮廓的数据库来训练这个模型时，它可以准确地预测人类对形状相似性的判断，而无需拟合任何人类数据的参数。为了测试该模型，我们使用经过动物轮廓训练的生成式对抗网络创建了精心挑选的复杂新颖形状数组，然后在广泛的任务中向观察者展示。我们的研究结果表明，结合多个 ShapeComp 维度可以促进对少数形状的人类形状相似性的预测，同时也可以捕捉到许多形状的多种排列的大部分方差。ShapeComp 优于传统的基于像素的指标和最先进的卷积神经网络，也可以用于生成感知均匀的刺激集，因此它是研究人类大脑中形状和物体表示的强大工具。
