Deep-learning methods can extract high-dimensional feature vectors for objects, concepts, images, and texts from large-scale digital data sets. These vectors are proxies for the mental representations that people use in everyday cognition and behavior. For this reason, they can serve as inputs into computational models of cognition, giving these models the ability to process and respond to naturalistic prompts. Over the past few years, researchers have applied this approach to topics such as similarity judgment, memory search, categorization, decision making, and conceptual knowledge. In this article, we summarize these applications, identify underlying trends, and outline directions for future research on the computational modeling of naturalistic cognition and behavior.

深度学习方法可以从大规模数字数据集中提取对象、概念、图像和文本的高维特征向量。这些向量是人们在日常认知和行为中使用的心理表征的代理。出于这个原因，它们可以作为认知计算模型的输入，使这些模型能够处理和响应自然提示。在过去的几年里，研究人员已经将这种方法应用于相似性判断、记忆搜索、分类、决策和概念知识等主题。在本文中，我们总结了这些应用，确定了潜在趋势，并概述了未来自然认知和行为计算建模研究的方向。