Psychological science can benefit from and contribute to emerging approaches from the computing and information sciences driven by the availability of real-world data and advances in sensing and computing. We focus on one such approach, machine-learned computational models (MLCMs)—computer programs learned from data, typically with human supervision. We introduce MLCMs and discuss how they contrast with traditional computational models and assessment in the psychological sciences. Examples of MLCMs from cognitive and affective science, neuroscience, education, organizational psychology, and personality and social psychology are provided. We consider the accuracy and generalizability of MLCM-based measures, cautioning researchers to consider the underlying context and intended use when interpreting their performance. We conclude that in addition to known data privacy and security concerns, the use of MLCMs entails a reconceptualization of fairness, bias, interpretability, and responsible use.

心理科学可以受益于计算和信息科学的新兴方法，并为这些方法做出贡献，这些方法由现实世界数据的可用性以及传感和计算方面的进步驱动。我们专注于一种这样的方法，机器学习计算模型 (MLCM)——从数据中学习的计算机程序，通常在人工监督下进行。我们介绍了 MLCM，并讨论了它们如何与心理科学中的传统计算模型和评估进行对比。提供了来自认知和情感科学、神经科学、教育、组织心理学以及人格和社会心理学的 MLCM 示例。我们考虑基于 MLCM 的测量的准确性和普遍性，提醒研究人员在解释其性能时要考虑潜在的背景和预期用途。