PredPsych: A toolbox for predictive machine learning based approach in experimental psychology research

Recent years have seen an increased interest in machine learning based predictive methods for analysing quantitative behavioural data in experimental psychology. While these methods can achieve relatively greater sensitivity compared to conventional univariate techniques, they still lack an established and accessible software framework. The goal of this work was to build an open-source toolbox – “PredPsych” – that could make these methods readily available to all psychologists. PredPsych is a user-friendly, R toolbox based on machine learning predictive algorithms. In this paper, we present the framework of PredPsych via the analysis of a recently published multiple-subject motion capture data set. In addition, we discuss examples of possible research questions that can be addressed with the machine learning algorithms implemented in PredPsych and cannot be easily investigated with mass univariate statistical analysis. We anticipate that PredPsych will be of use to researchers with limited programming experience not only in the field of psychology, but also in that of clinical neuroscience, enabling computational assessment of putative bio-behavioral markers for both prognosis and diagnosis.