文章基本信息

标题：Machine Learning in Psychometrics and Psychological Research
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作者：Orrù, Graziella ; Monaro, Merylin ; Conversano, Ciro 等
期刊名称：Frontiers in Psychology
电子版ISSN：1664-1078
出版年度：2020
卷号：10
页码：1-10
DOI：10.3389/fpsyg.2019.02970
出版社：Frontiers Media
摘要：Recent controversies about the level of replicability of behavioral research analyzed using statistical inference have cast interest in developing more efficient techniques for analyzing the results of psychological experiments. Here we claim that complementing the analytical workflow of psychological experiments with Machine Learning-based analysis will both maximize accuracy and minimize replicability issues. As compared to statistical inference, ML analysis of experimental data is model agnostic and primarily focused on prediction rather than inference. We also highlight some potential pitfalls resulting from adoption of Machine Learning based experiment analysis. If not properly used it can lead to over-optimistic accuracy estimates similarly observed using statistical inference. Remedies to such pitfalls are also presented such and building model based on cross validation and the use of ensemble models. ML models are typically regarded as black boxes and we will discuss strategies aimed at rendering more transparent the predictions.
关键词：machine learning; Cross-validation; replicability; Machine Learning in Psychological experiments; Machine Learning in Psychometrics