Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning
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- Tal Yarkoni
- University of Texas at Austin
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- Jacob Westfall
- University of Texas at Austin
書誌事項
- 公開日
- 2017-08-25
- 権利情報
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- https://journals.sagepub.com/page/policies/text-and-data-mining-license
- DOI
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- 10.1177/1745691617693393
- 公開者
- SAGE Publications
この論文をさがす
説明
<jats:p> 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. </jats:p>
収録刊行物
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- Perspectives on Psychological Science
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Perspectives on Psychological Science 12 (6), 1100-1122, 2017-08-25
SAGE Publications