University of Texas at Austin.
Perspect Psychol Sci. 2017 Nov;12(6):1100-1122. doi: 10.1177/1745691617693393. Epub 2017 Aug 25.
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.
心理学历史上首先关注的是解释导致行为的因果机制。随机、严格控制的实验被奉为心理学研究的金标准，人们对各种中介和调节变量进行了无休止的研究，这些变量控制着各种行为。我们认为，心理学几乎完全专注于解释行为的原因，这导致该领域的大部分研究项目都提供了关于心理机制的复杂理论，但几乎没有（或未知）能力以任何可衡量的准确性预测未来的行为。我们提出，机器学习领域的原理和技术可以帮助心理学成为一门更具预测性的科学。我们回顾了机器学习的一些基本概念和工具，并指出了这些概念在哪些地方被用于进行有趣且重要的关注预测性研究问题的心理学研究。我们认为，更多地关注预测而非解释最终可以使我们更好地理解行为。
