Using the Availability Bias

The more you practice using a certain tool or skill, the more comfortable you become performing it. You build up muscle memory, and the motions become automatic. You can focus on more challenging topics. If you can repeat keystrokes without exhausting conscious effort on writing software, then you have that much more energy and attention available to devote to more advanced and abstract concerns.

This automation process is extremely helpful, and allows our brains to operate on more challenging issues as we work toward mastery. But relying on the information our brains supply for us leaves us vulnerable to its subtle shortcomings; sometimes the solution that presents itself automatically isn’t the best solution.

These shortcomings can be summed up as the Availability Bias, in which we assign inflated confidence to the information that readily presents itself to us. In order to avoid the traps of a logical fallacy like this, it helps to understand how it’s created.

Our brains can only store a limited amount of information at any one time. Names, faces, dates, and places get shuffled around and lost in our heads in order to make room for new information every day. These limitations are even more imposing on the number of thoughts and memories that are immediately accessible through our working memories. We can’t hoard memories—our brains are constantly sifting through and discarding pieces of them.

Recent literature suggests there’s an evolutionary motor behind this process. Current theories propose that, because we were born with physical restrictions on how much information we can store in our brains, they come equipped with a natural updating mechanism that ensures we’re only carrying information that continues to be relevant. Studies have shown that whenever we recall a memory, the details that were unimportant to us do not appear in future recalls of the same memory.

For long-term memory, that means we forget the names of people we went to school with and haven’t thought about in years; or we consolidate several memories about different recess periods into a single one that is more compact.

For short-term or working memory, that means the information readily available to us is the information that has proven itself to be useful in similar situations recently. Our brains gather sensory information to prepare and front-load a pool of information that it thinks will be advantageous. If we’re baking a cake, our working memory loads everything we know about measuring, mixing, and baking to our awareness. Our mind frame shifts with each new task, carrying over the tools that we’ve needed in the past.

When working, we rely on this process to help us solve problems quickly and easily. It is an exceedingly helpful shortcut that we would struggle without. It allows us to practice certain skills until they are automatic, and then rely on those skills to operate well without expending energy thinking about them again.

But this process of relying on connections that we’ve made in the past assumes that they will remain consistent with the problems we will face in the future. Every time we approach a new problem, we’ve already made assumptions about its answer. We arrive at every new problem with one arm tied behind our back (or, with the majority of our knowledge distant and inaccessible).

This is how we fall victim to the Availability Bias. In our attempt to automate a portion of labor, our problem solving skills forfeit some amount of scrutiny. But if we acknowledge that this bias exists, we can confront it and take advantage of it in two ways.

1. We should actively seek out and learn new information that is irrelevant to our work. By learning new information that exists far away from what we are doing, we can disrupt the comfort zone created by our working memory and force ourselves to consider new ideas. While we’re learning new information, we can look for ways to fold this new information into our existing contexts. For example: if we practice Clojure, we can look for ways to incorporate techniques from its functional paradigm into our Ruby code.

Those kinds of connections are impossible unless we force an overlap between separate, unrelated paradigms. When our available context is broadened, we allow ourselves the potential to spark new connections and ideas that change the way we think for the better. We teach our brains to recognize that a wider swath of knowledge is advantageous in each given setting.

2. We should be revisiting old information regularly. Most professionals return to old materials to refresh themselves before jumping into a new project, but there’s utility to this practice even during times when you don’t anticipate needing to use the new information.

This is one of the biggest appeals of mentorship. Teaching a topic to someone else requires that you have total comprehension. Before teaching a lesson, you’ll need to return to the material and reacquaint yourself with its details. This reminds your brain what it already knows, and helps keep the information fresh and easily accessible to your subconscious.

Mentoring others isn’t entirely altruistic. Mentorship helps us maintain a close relationship with our tools and skills, and thus ensures our working memories remain ours to take advantage of.

The reality is that we can never fully anticipate what problems we’ll encounter in our work, and we can’t stretch our working memories wide enough to include everything we might possibly need to know. But by acknowledging the Availability Bias and deliberately diversifying the types of practices and lessons that float around in our mind, we can make sure that the information that presents itself to us is at least representative of a larger sample of our brains.