Classifying Intelligence Tests Patterns Using Machine Learning Methods

Abstract

Intelligence testing assesses a variety of cognitive abilities and is frequently used in the evaluation of people for jobs, army recruitment, scholarships, and the educational system in general. Licensed psychologists and researchers create and analyze intelligence tests, setting the difficulty layer, grading them, and weighing the results on a global scale. However, developing new model tests is a time-consuming and challenging process. In this study, we lay the groundwork for developing a model that classifies the IQ patterns, in order to generate new IQ Raven tests. More specifically, we analyze Raven’s Progressive Matrices Tests, a nonverbal multiple-choice intelligence test, and their patterns using a variety of Machine Learning (ML) techniques. In such intelligence tests, the question's data includes mostly abstract images aligned in a grid system, with one missing element and a pattern that connects them by threes in horizontal and vertical order. These tests have been labeled based on several factors, such as the number of images, the type of pattern (e.g. counting, adding, or rotating), or their complexity and in order to classify them, various ML methods are used. Results of the current study act as a defining basis for the use of advanced Neural Network models, not only for classification but also for the generation of new IQ patterns.