Quantitative, data-driven models for mental representations have long enjoyed popularity and success in psychology (e.g., distributional semantic models in the language domain), but have largely been missing for the visual domain. To overcome this, we present ViSpa (Vision Spaces), high-dimensional vector spaces that include vision-based representation for naturalistic images as well as concept prototypes. These vectors are derived directly from visual stimuli through a deep convolutional neural network trained to classify images and allow us to compute vision-based similarity scores between any pair of images and/or concept prototypes. We successfully evaluate these similarities against human behavioral data in a series of large-scale studies, including off-line judgments—visual similarity judgments for the referents of word pairs (Study 1) and for image pairs (Study 2), and typicality judgments for images given a label (Study 3)—as well as online processing times and error rates in a discrimination (Study 4) and priming task (Study 5) with naturalistic image material. ViSpa similarities predict behavioral data across all tasks, which renders ViSpa a theoretically appealing model for vision-based representations and a valuable research tool for data analysis and the construction of experimental material: ViSpa allows for precise control over experimental material consisting of images and/or words denoting imageable concepts and introduces a specifically vision-based similarity for word pairs. To make ViSpa available to a wide audience, this article (a) includes (video) tutorials on how to use ViSpa in R and (b) presents a user-friendly web interface at http://vispa.fritzguenther.de.

中文翻译：

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ViSpa（视觉空间）：基于计算机视觉的单个图像和概念原型的表示系统，具有大规模评估功能。

定量的、数据驱动的心理表征模型长期以来在心理学领域广受欢迎并取得成功（例如，语言领域的分布式语义模型），但在视觉领域却基本上缺失。为了克服这个问题，我们提出了 ViSpa（视觉空间），即高维向量空间，其中包括自然图像的基于视觉的表示以及概念原型。这些向量是通过经过训练对图像进行分类的深度卷积神经网络直接从视觉刺激中导出的，并允许我们计算任何一对图像和/或概念原型之间基于视觉的相似性分数。我们在一系列大规模研究中成功地根据人类行为数据评估了这些相似性，ViSpa 的相似性可以预测所有任务中的行为数据，这使得ViSpa成为基于视觉的表示的理论上有吸引力的模型，以及用于数据分析和实验材料构建的有价值的研究工具：ViSpa允许精确控制由图像和/或组成的实验材料表示可成像概念的单词，并引入了单词对的基于视觉的特定相似性。为了使ViSpa可供广大受众使用，本文 (a) 包含有关如何在 R 中使用ViSpa 的（视频）教程，并且 (b) 在 http://vispa.fritzguenther.de 上提供了一个用户友好的 Web 界面。