Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning
Item
Title
Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning
Abstract/Description
Psychology has historically been concerned, first and foremost, with explaining the causal mechanisms that give rise to behavior. Randomized, tightly controlled experiments are enshrined as the gold standard of psychological research, and there are endless investigations of the various mediating and moderating variables that govern various behaviors. We argue that psychology’s near-total focus on explaining the causes of behavior has led much of the field to be populated by research programs that provide intricate theories of psychological mechanism but that have little (or unknown) ability to predict future behaviors with any appreciable accuracy. We propose that principles and techniques from the field of machine learning can help psychology become a more predictive science. We review some of the fundamental concepts and tools of machine learning and point out examples where these concepts have been used to conduct interesting and important psychological research that focuses on predictive research questions. We suggest that an increased focus on prediction, rather than explanation, can ultimately lead us to greater understanding of behavior.
Author/creator
Date
In publication
Volume
12
Issue
6
Pages
1100-1122
Resource type
Background/Context
Medium
Print
Background/context type
Conceptual
Open access/free-text available
Yes
Peer reviewed
Yes
ISSN
1745-6916
Citation
Yarkoni, T., & Westfall, J. (2017). Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning. Perspectives on Psychological Science, 12(6), 1100–1122. https://doi.org/10.1177/1745691617693393
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