AUTHOR=Orrù Graziella , Monaro Merylin , Conversano Ciro , Gemignani Angelo , Sartori Giuseppe TITLE=Machine Learning in Psychometrics and Psychological Research JOURNAL=Frontiers in Psychology VOLUME=10 YEAR=2020 URL=https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2019.02970 DOI=10.3389/fpsyg.2019.02970 ISSN=1664-1078 ABSTRACT=

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.