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

• Published in: Behavior research methods Vol. 50; no. 4; pp. 1657 - 1672
• Main Authors: Koul, Atesh, Becchio, Cristina, Cavallo, Andrea
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2018
• Subjects: Behavioral Research - methods; Behavioral Science and Psychology; Cognitive Psychology; Data Interpretation, Statistical; Humans; Machine Learning; Psychology; Psychology, Experimental - methods; Permutation testing; Predictive approaches; Multivariate analysis; Clustering; Classification
• ISSN: 1554-3528; 1554-351X; 1554-3528
• DOI: 10.3758/s13428-017-0987-2

Recent years have seen an increased interest in machine learning-based predictive methods for analyzing quantitative behavioral data in experimental psychology. While these methods can achieve relatively greater sensitivity compared to conventional univariate techniques, they still lack an established and accessible implementation. The aim of current work was to build an open-source R 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 dataset. 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 addressed with 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.