The book provides an accessible review of current neuroscience findings on how our brains learn various skills. Throughout the book, the author also compares our "learning algorithms" to state of the art machine learning approaches. Through the comparison, it becomes clearer why machines can't (yet) learn as efficiently as we do, but the author also hints at how newer, more efficient algorithms are becoming better at imitating our biological learning software. In the final third of the book, the author explores the four pillars of learning – attention, active engagement, error feedback, and consolidation –, which offer practical guidance on how to apply the presented neuroscience findings to teaching and learning.

Overall, the weakest part of the book is the introductory chapter. Luckily, the rest of the book is well-argued, rich in resources from diverse fields of research, and the final evidence-based recommendations for parents, teachers, and designers of educational systems pave the way to valuable real-life applications of the science of learning.