As a PhD student in Machine Learning, I have noticed there is a pattern of distraction that seems to happen more often than any other. I will refer to it as the “barrier of uncertainty”. It goes something like this.
I’m working on a piece of code or on some experiments, and things are going smoothly. Suddenly I hit a roadblock, where there is a strange bug in my code that I can’t seem to figure out, an experiment that mysteriously doesn’t work, or I just found some result that calls into question my whole project. Even though this type of thing is the bread and butter of a researcher, it usually has catastrophic consequences for my productivity.
As I run into one of these problems, my brain’s immediate reaction is to try to find comfort in a distraction. Maybe reach for the phone, maybe check what’s on /r/wallstreetbets, or maybe check what’s the latest Twitter outrage. The task seems too hard for my brain to properly process, and I immediately lose all motivation to keep working. More worryingly, this happens regardless of the actual difficulty of the problem - it’s not like I only check my phone when the solving the Riemann Hypothesis is a requirement for finishing my paper.
It seems that the very existence of a roadblock pushes my brain to crave procrastination. More often than not, this results in nothing being done for the rest of the day, as I can’t shake off the feeling that I should be solving this problem (as it is a requirement for me to move forward), but at the same time knowing that there might a huge mountain to climb saps off all my mental energy.
I am still very much trying to figure out how to best overcome the inertia that comes with knowing that I have to tackle a really hard problem for which I do not the full solution in advance (or for which a solution might not even exist), but I wanted to share a method that has been working for me.
When I run into a problem and I see my brain retreating, I make a list with all the steps needed to complete the task - the ones I can think of, at least. The more specific the better. This turns an otherwise seemingly gargantuan task into many tiny tasks. In my experience, most of these smaller tasks will be much more manageable, so instead of thinking “Today I’ll solve problem A”, I think “Today I’ll do task B”. Since B is very short and self-contained, it doesn’t cause the same “I’d rather be in a wrestling cage with a gorilla than think about problem A”-reaction.
When I follow this strategy, what often ends up happening is that once I get started on the first task, positive momentum builds, and I end up tackling many more of the tasks than I had anticipated. Soon enough, the whole problem has been solved, or if it turns out to be unsolvable or misguided, at least I found out, which is also progress - I wouldn’t have found out had I not started.
This type of action point list isn’t anything new if you work at a company. If you are an engineer, you will have a manager who will help you determine long-term goals, define daily or weekly objectives, and formulate exact To-Do lists to achieve them. If you get stuck, you can always ask for help, as your manager/team will likely be heavily invested in unblocking your progress. In academia, however, work is much more self-guided, with your supervisors or collaborators helping you define high-level objectives, but the nitty gritty of the implementation is up to you. This means you have to micro-manage yourself. Writing highly-specific action points allows you to act as your own manager, and then all you have to do is follow the steps that your “manager” proposed. It’s like roleplaying for PhD students.