PsAIchology: An Intelligent Direction in Psychological Sciences

Abstract

The intersection of machine learning (ML) and cognition is often referred to as 'artificial intelligence', whereas the intersection of psychology and ML is a term we would like to coin as 'Artificial Psychology' or “PsAIchology”. The main purpose of this paper is to introduce three commonly used machine learning algorithms for mind research along with their R codes. This paper aims not only to introduce these methods for analyzing data but also tries to provide the answers for questions that may arise for a mind researcher including a) how to choose which algorithm needs to be used for a given dataset, b) How to implement them using R code, c) How to assess model performance to select the best performing algorithm and d) How to interpret the results of the ML algorithms obtained from fitting to a set of data. In this paper, we introduce and illustrate the most commonly used ML algorithms including, AdaBoost, Extreme Gradient Boosting (XGBoost), Random forest and give related R codes with the results obtained from running them. Finally, model performance is interpreted and discussed.