Exploring Psychological Data by Integrating Explanatory and Predictive Approaches through Artificial Neural Networks: A Brief Overview of Current Applications

Traditional psychometric data analysis techniques belong to an explanatory approach and often require several assumptions on data. In contrast, Machine Learning (ML) techniques belong to a predictive approach and necessitate mild assumptions on input data. These predictive techniques aim to identify data patterns and generate accurate predictions of output values based on input values of new observations. In recent years, several works proposed the integration of explanatory and predictive approaches. This paper provides an overview of the works carried out at the Natural and Artificial Cognition “Orazio Miglino” Lab, discussing various applications of Artificial Neural Networks in psychology. The discussed studies highlight the promising outcomes of integrating machine learning techniques into traditional psychometric data analysis. Specifically, due to their flexible assumptions on input data, their ability to handle different types of input data, and their ability to model complex and nonlinear relationships between variables, the integration of ML techniques could complement and enhance psychological data analysis.