Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning.
Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning.
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DOI：
10.1177/1745691617693393
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发表时间：
2017-11
期刊：
影响因子：
--
通讯作者：
Westfall J
中科院分区：
文献类型：
--
作者：
Yarkoni T;Westfall J
从历史上看，心理学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黄金标准，对控制各种行为的各种中介和调节变量的研究也层出不穷。我们认为，心理学几乎完全专注于解释行为的原因，导致该领域的许多研究项目充斥着提供复杂的心理机制理论，但几乎没有（或未知的）能力以任何可观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，并指出了这些概念被用于进行有趣和重要的心理学研究的例子，这些研究的重点是预测研究问题。我们认为，更多地关注预测，而不是解释，最终可以让我们更好地理解行为。
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.
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