A central goal in the cognitive sciences is the development of numerical models for mental representations of object concepts. This paper introduces Variational Interpretable Concept Embeddings (VICE), an approximate Bayesian method for embedding object concepts in a vector space using data collected from humans in a triplet odd-one-out task. VICE uses variational inference to obtain sparse, non-negative representations of object concepts with uncertainty estimates for the embedding values. These estimates are used to automatically select the dimensions that best explain the data. We derive a PAC learning bound for VICE that can be used to estimate generalization performance or determine a sufficient sample size for experimental design. VICE rivals or outperforms its predecessor, SPoSE, at predicting human behavior in the triplet odd-one-out task. Furthermore, VICE's object representations are more reproducible and consistent across random initializations, highlighting the unique advantage of using VICE for deriving interpretable embeddings from human behavior.
翻译:认知科学的一个中心目标是为物体概念的心理表现开发数字模型。本文件介绍了一种可解释概念嵌入的变式模型(VICE),这是一种使用从人类收集的数据,在三重单方位任务中将物体概念嵌入矢量空间的近似巴伊西亚方法。 VICE使用变式推论,以获得稀疏、非负式的物体概念的表达方式,同时对嵌入值进行不确定性估计。这些估计用于自动选择能解释数据的维特维特。我们为VICE获取了一种PAC学习,可以用来估计一般化性能或确定足够用于实验设计的样本大小。VICE在预测三重单位任务中的人类行为时,比其前身SPOSE相匹配或超出其前身SPOSE。此外,VICE的物体表达方式在随机初始化中更加重复和一致,突出了使用VICE从人类行为中获取可解释的嵌入数据的独特优势。