The study of visual concept learning methodologies has been developed over the last years, becoming the state-of-the art research that challenges the reasoning capabilities of deep learning methods. In this paper we discuss the evolution of those methods, starting from the captioning approaches that prepared the transition to current cutting-edge visual question answering systems. The emergence of specially designed datasets, distilled from visual complexity, but with properties and divisions that challenge abstract reasoning and generalization capabilities, encourages the development of AI systems that will support them by design. Explainability of the decision making process of AI systems, either built-in or as a by-product of the acquired reasoning capabilities, underpins the understanding of those systems robustness, their underlying logic and their improvement potential.