Here are some selected readings to enhance your understanding of cognitive processes, strengthen your ability to work with deep concentration and minimal distraction, and sharpen your data interpretation skills. This exploration is crucial to developing the analytical, evaluative, and intellectual skills necessary for your successful doctoral journey.
Thinking Fast and Slow, by Kahneman
It is always necessary to know about the tool we are working with. For example a cyclist benefits from knowing how his bike works. A PhD student works mainly with his brain. It is his main tool. And this book offers a complete exposition of how it works and the limitations it has.
If you prefer a gentler approach to understanding the ideas of Kahneman and Tversky in a narrative style, you may find it enjoyable to start with "The Undoing Project: A Friendship That Changed Our Minds" by Michael Lewis. Additionally, the movie "Moneyball," starring Brad Pitt, offers insights into the application of these ideas in the world of sports. And here a Vanity Fair article: Decision Science: Daniel Kahneman and Amos Tversky.
Deep Work, by Newport
The book provides valuable insights into achieving a high level of focus, vital for research-intensive activities. Newport establishes the importance of 'Deep Work' - the ability to focus without distraction on a cognitively demanding task, a skill crucial for advancing in academic studies.
If you're interested in enhancing your learning skills further, I highly recommend enrolling in the course "Learning How to Learn: Powerful Mental Tools to Help You Master Tough Subjects" on Coursera.
How to Make the World Add up, by Harford
Harford effectively demystifies the challenges of understanding and interpreting statistics, which is a fundamental skill in research. The book navigates through overcoming biases, adopting critical thinking, and exposes the reader to some fascinating experiences about the application of statistics in many diverse fields. It can help instill a more informed, rigorous, and skeptical approach towards data interpretation – a critical asset in any PhD journey.