Deep learning: A primer for psychologists

• Published in: Psychological methods Vol. 26; no. 6; p. 743
• Main Authors: Urban, Christopher J, Gates, Kathleen M
• Format: Journal Article
• Language: English
• Published: United States 01.12.2021
• Subjects: Deep Learning; Humans; Natural Language Processing; Neural Networks, Computer
• ISSN: 1939-1463
• DOI: 10.1037/met0000374

Deep learning has revolutionized predictive modeling in topics such as computer vision and natural language processing but is not commonly applied to psychological data. In an effort to bring the benefits of deep learning to psychologists, we provide an overview of deep learning for researchers who have a working knowledge of linear regression. We first discuss several benefits of the deep learning approach to predictive modeling. We then present three basic deep learning models that generalize linear regression: the feedforward neural network (FNN), the recurrent neural network (RNN), and the convolutional neural network (CNN). We include concrete toy examples with R code to demonstrate how each model may be applied to answer prediction-focused research questions using common data types collected by psychologists. (PsycInfo Database Record (c) 2022 APA, all rights reserved).