Objects can be characterized according to a vast number of possible criteria (such as animacy, shape, colour and function), but some dimensions are more useful than others for making sense of the objects around us. To identify these core dimensions of object representations, we developed a data-driven computational model of similarity judgements for real-world images of 1,854 objects. The model captured most explainable variance in similarity judgements and produced 49 highly reproducible and meaningful object dimensions that reflect various conceptual and perceptual properties of those objects. These dimensions predicted external categorization behaviour and reflected typicality judgements of those categories. Furthermore, humans can accurately rate objects along these dimensions, highlighting their interpretability and opening up a way to generate similarity estimates from object dimensions alone. Collectively, these results demonstrate that human similarity judgements can be captured by a fairly low-dimensional, interpretable embedding that generalizes to external behaviour.

可以根据大量可能的标准（例如生命、形状、颜色和功能）来表征物体，但对于理解我们周围的物体，某些维度比其他维度更有用。为了识别对象表示的这些核心维度，我们开发了一个数据驱动的计算模型，用于对 1,854 个对象的真实世界图像进行相似性判断。该模型捕获了相似性判断中最可解释的方差，并生成了 49 个高度可重复且有意义的对象维度，反映了这些对象的各种概念和感知属性。这些维度预测了外部分类行为并反映了这些类别的典型性判断。此外，人类可以沿着这些维度准确地评价对象，突出它们的可解释性，并开辟了一种仅根据对象维度生成相似性估计的方法。总的来说，这些结果表明，人类的相似性判断可以通过相当低维的、可解释的嵌入来捕获，并推广到外部行为。