Cognitive Modeling With Representations From Large-Scale Digital Data. (June 2022)

• Record Type: Journal Article
• Authors: Bhatia, Sudeep; Aka, Ada
• Is Part Of: Current directions in psychological science. Volume 31:Number 3(2022)
• Page Start: 207
• Page End: 214
• Publication Date: 2022-06
• Subjects: cognitive modeling -- deep learning -- big data -- computational methods; Psychology -- Periodicals; 150
• DOI: 10.1177/09637214211068113
• Languages: English
• ISSNs: 0963-7214

Abstract

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.