分类
认知
相似性(几何)
心理学
过程(计算)
计算模型
认知科学
比例(比率)
自然主义
特征(语言学)
计算机科学
认知心理学
人工智能
数据科学
认识论
图像(数学)
物理
哲学
神经科学
操作系统
量子力学
语言学
标识
DOI:10.1177/09637214211068113
摘要
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.
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