Applications of machine learning to behavioral sciences: focus on categorical data

Abstract

AbstractIn the last two decades, advancements in artificial intelligence and data science have attracted researchers' attention to machine learning. Growing interests in applying machine learning algorithms can be observed in different scientific areas, including behavioral sciences. However, most of the research conducted in this area applied machine learning algorithms to imagining and physiological data such as EEG and fMRI and there are relatively limited non-imaging and non-physiological behavioral studies which have used machine learning to analyze their data. Therefore, in this perspective article, we aim to (1) provide a general understanding of models built for inference, models built for prediction (i.e., machine learning), methods used in these models, and their strengths and limitations; (2) investigate the applications of machine learning to categorical data in behavioral sciences; and (3) highlight the usefulness of applying machine learning algorithms to non-imaging and non-physiological data (e.g., clinical and categorical) data and provide evidence to encourage researchers to conduct further machine learning studies in behavioral and clinical sciences.