Multitasking: Serial vs. Parallel Learning

At some point in your life, somebody probably told you to "learn how to multi-task". So, as you grew up, you did just that. You began to parallelize your workload by eating while studying and listening to music while working out. But, at what point does multitasking become less effective and more distracting? Two important topics come into play here, the automaticity of tasks and serial vs parallel learning.

Among the examples I used before, you'll notice that eating and listening to music are decidedly secondary actions when compared to studying and working out. To your brain, these tasks are relatively trivial because they have high automaticity. Over your lifetime you have repeated these actions thousands of times, so your brain recognizes and caches these activities to minimize the resources and attention you need to perform them. This way, if you automatically allocate 10% of your attention for secondary tasks, you can channel the remaining 90% into primary tasks simultaneously.

"As we get used to the tasks and they become more familiar, less brain attention is required to perform them" (Baron, Illinois.edu).

Now you might say, "wait a minute Felix... wouldn't it be better if I used all 100% of my brain for these primary tasks?" Well, that's not necessarily true. Two major reasons people multitask are 1. to save time, and 2. to make something more entertaining. When you parallelize two tasks you essentially save the amount of time equivalent to the shorter of the two activities. If you study for 1 hour and eat for 30 minutes, you could multitask and take 1 hour instead of 1.5 hours for both tasks. If you are on a tight schedule and the time tradeoff is worth the small negative impact that a secondary task has on your primary focus, then multitasking is beneficial. Or, if adding an entertaining secondary task helps you maintain a longer attention span, it might help to parallelize your workload. Operating at 90% for 2 hours is better than at 100% for 1 hour.

Now, this leads us into the meat of the multitasking discussion. What happens if you want to do two primary tasks at once? These activities are impossible to parallelize the same way as I described with secondary tasks. Whether you are working on multiple side projects, studying for two classes, or playing sports, these tasks take up so much brain power that 'multitasking' now becomes 'pseudo-multitasking', which occurs when people start and stop portions of each activity in order to appear to be doing multiple at once. This is an instance of what I call parallel learning. Parallel learners target a wide breadth of knowledge instead of a deep exploration of each topic. In contrast, serial learners do not attempt to pseudo-multitask. They concentrate on the thorough completion of each task before moving on. Each of these cases might work better in specific scenarios, but if you want to learn multiple topics and also understand them deeply, serial learning is a much more effective.

To better explain why this is so, let us take a break from all this talk about multitasking to paint an analogy. Imagine a large, dry surface out in the open. One day it rains. If that land is completely flat, the water will spread out perfectly as one thin sheet across the surface and dry up quickly. However, if there is a single crack in the surface, the water will naturally gravitate towards that area. Even after the water dries the crack will be deeper and wider than it was before, and every time it rains, the trickle that once ran through it will become a stream, then a river. As the crack grows, more water will flow through it, making the current stronger and harder to dry up. Over time, the river will form cracks along its own banks, creating a wider network of distributaries. On the other hand, a perfectly flat surface will require much longer and harder precipitation for the thin sheet to even grow into a pond, let alone something larger. Serial learning is similar to the formation of a river. It starts with a crack into a single topic. If you concentrate all your time and energy into this topic, your knowledge about that subject will deepen every time you work at it. Over time, learning becomes easier and faster as your knowledge increases, allowing your progress to rapidly speed up and, because all subjects are interconnected, you will soon find yourself branching out into distributaries that will connect to new knowledge streams. Parallel learning is similar the rain on the flat surface. If you try to cover too much ground at the same time, it take much more time and effort to make any significant progress.

Serial Learning (River Network) vs Parallel Learning (Water on flat surface):

If the learning curve for a single topic takes 10 days, after those 10 days you will find it much easier and more satisfying to learn that subject. If you parallelize your learning, or pseudo-multitask, you will have to spend significantly more time reaching that threshold because you are attempting a multiple projects at once. Trying to do bits and pieces of 5 primary tasks will require more than 5x as much time to get over each individual task's learning curve. While serial learning will make things quicker and easier to understand over a period of time, parallel learning can become cumbersome and unrewarding. If we use the same 10 day learning curve in the previous example, it will take more than  50 days before you start feeling satisfied with your progress in any one topic because your dedication will wear out over such a long period of time. 

It is time to stop pretending to do so many things at once and concentrate on the task at hand. Understanding things deeply is more natural than understanding a wide variety of them. Why do you think there are more people who are well-accomplished at fewer tasks than there are who succeed at many tasks? Or, for my fellow CS geeks, why is depth first recursion more natural to implement than a breadth first function? (just some food for thought). We are not built like computers, we don't have multi-core processing capabilities built into our heads. We have one central neural system and it lowers its processing ability for each task you try to do simultaneously. While serializing your workload might seem scary, it is actually more natural to concentrate on primary tasks one at a time. So, get rid of that nasty procrastination habit that forces you to multitask as you near your deadlines. Start things early and work at them more deeply. You will probably find a deeper network of learning that is much more rewarding.