Connecting Our Brains With Technological Change

Suppose John had been working at the same business in the next town for 10 years, and it always seemed to take him about 25 minutes to drive to work. One day John met a new employee, and found out she lived in his neighborhood. The next morning as John pulled out of his driveway to go to work, he turned north as usual, but saw his neighbor turning south. Naturally, John assumed she was going somewhere other than the office, but when he arrived, she was at her desk drinking a cup of coffee. How would he react?

Most likely he would start with disbelief, and then move to denial (it must not have been her turning south out of the neighborhood that morning). But eventually he might have a conversation and discover that she had found a route to work that he had never considered. A new freeway had opened in the past several years, but he had never tried it. The new route cut the commute time to 15 minutes.

This little scenario sounds preposterous. Could anyone be as unaware as John?

In fact, variations of this little scenario play out every day, in a large part because of the way technology and our brains intersect.

Technology and the Brain

What we learn from brain science (e.g., in Brain Rules) and psychology (e.g., in Thinking Fast and Slow) is that our brains are excellent at pattern matching. (Both fields deal with the study of the brain, but brain science looks inside the brain at the physiology of its behavior, while psychology looks at the brain from the outside, treating it as a “black box.”) When we encounter a new situation, we immediately try to match that situation with something we have seen before. Once we recognize it, we don’t have to think deeply about it at all. We can simply react. John had developed a pattern for how he went to work, and didn’t need to think about it again.

Where technology enters the picture is that it changes things, often dramatically. Of course it is not the only change agent, but its impact on modern business and life is profound, and many introductions of technology rattle the patterns in our brains. It is like new freeways being built on a regular basis in many processes in our lives. Our brain encourages us to either perpetuate old patterns or resist new ones because they don’t align. It keeps us from looking for alternatives to our preconceived notions, because we already know the pattern for many particular situations.

“Many introductions of technology rattle the patterns in our brains.”


Let me offer some illustrations.

A web page. In the mid-1990s, a colleague at Boeing left the company to start his own business. The first thing he needed to do was create a web page, because his new business was going to be one of the emerging dot-coms. Three months later, after finally completing his new web page, he discovered some software tools that had recently been developed. Using these tools, he found that he could have created his web page, not in three months but in half a day.

Taxes and jobs. In a recent column, I wrote about the role of technology in a jobless recovery. A “catch phrase” used by many politicians is that raising taxes on the rich destroys jobs while lowering taxes creates jobs. That may have been close to true at one time, but in an era of globalization, technology, and consumption, there is a significant reason to doubt the veracity of this statement.

Enterprise systems. Standard processes in a business enable the business to produce things (a manufactured product, a bill of material, a customer order) much faster, with lower cost, and higher quality. This is the reason many companies have gone through the pain and cost of putting in an enterprise system. But the history of implementing such systems has been filled with cost overruns and outright failures, in a large part because of resistance to change. The new way of doing things does not fit the old patterns or models in the heads of people who need to learn a new way of doing their job.

Mathematical software. In the 1960s, the technical field of mathematical software began, and today it is an active area of research with journals, conferences, and commercial products. Basically it is the construction of high quality mathematical algorithms that solve classes of mathematical problems efficiently, reliably, and accurately. These modules could be used by anyone constructing a mathematical model — from a quantum-physics simulation to a complex circuit design to the earthquake-proof design of a new building.

The motivation for this field of mathematical software was that the reuse of these mathematical software modules would allow anyone building a mathematical model to use these modules, thus making their model much more efficient and reliable. A further benefit was the time savings in building the model because the very complex task of building the module goes away, being done once by an expert and subsequently reused by many. These were available for free.

Over the years, however, all of the assumptions that went into the founding of this field have changed. For example, most people doing modeling today use a packaged modeling product that they buy, rather than software they build themselves. Most mathematical software collections are now sold with intellectual property protection inhibiting reuse, undermining the very purpose that motivated them in the first place. The assumptions that were established long ago, many of them no longer valid, have not been revisited.

It would not be difficult to fill pages with diverse examples from business and society, illustrating the way our standard assumptions, and our understanding of how things work, are no longer valid. But we work from them anyway. Why is this?

Why We Do This

Going back to brain science, we learn that our brains function the way they do for a very good reason. We are bombarded by far too much information to pay equal attention to all of it. So for millions of years our brains have developed to allow us to filter out much of the information that doesn’t really matter, and focus on those things that do. This filtering takes place through the patterns or models in our brains. They respond to the question, “Have I seen it before?” and if so, we don’t have to expend much brain energy to know what to do about the situation.

Origin of patterns. Where do these models or patterns come from? Some are developed through very careful thought. We have analyzed a collection of data and come to a rational conclusion, and our brain is prepared when we encounter the situation again. But brain scientists and psychologists tell us that many of the models in our brains have been developed much more implicitly. They are based on “tacit assumptions,” an understanding about the world that we have not formally identified, but that may be simply a part of the lore of our family, our culture, or the environment at a particular time. Prejudices come from such patterns in our brains.

An old illustration of a tacit assumption is helpful here. A woman is preparing to fix a beef roast, and before putting it in the pot, she cuts off the end. Her son asks her why she did that and she says, “That’s the way to prepare a roast (a tacit assumption). I learned this from my mother.” And like a good son, developing the patterns in his own brain, he said, “But why?” So the mother agreed to ask her mother, and they learned that she cut off the tip of the roast because she never had a pan big enough to hold the whole thing!

There is another side to this story. Just because we have always done something a certain way, doesn’t mean this is a good reason for not doing it that way. Thus we must be careful when we rethink how to do something. Other tacit assumptions may not be written down, indeed it would be impossible to write down all such assumptions, but they may still be important.

In looking at how to create a lower-cost airplane, a constant pressure from the airlines, a young Boeing engineer found that he could create a design using aluminum alloy parts in place of titanium parts, lowering both the cost and the weight of the airplane. Nowhere had it been written down that the parts needed to be titanium because of corrosion and safety issues. Fortunately the good safety-related reviews at the company discovered this before it left the design stage.

Dealing with change. The intersection between technology and the brain is a complicated one. Those models in our heads are there for a very good reason. Some of them, because of changes often driven by technology change, need to be rethought. Some of them could be rethought but should not. How do we know the difference?


If I could answer that question, I would be a very wealthy person. But there are a few clues. Just because we can rethink the assumptions and the way we do things doesn’t mean we must. We are all juggling far too many issues to rethink them all. And, as indicated, this is a challenging and difficult process involving making explicit the assumptions that went into a decision and working against what our brains want to do in taking the simpler path.

Going back to the opening scenario, the fact that John could have saved 10 minutes on his commute may not be all that important. A relaxing drive in the countryside may be better than a faster drive on a freeway. All things are not measured by efficiency.

That said, there are many areas where it really does matter to do things in a new way. In those cases, we must know that we have a difficult process ahead of us. We need to understand the tacit assumptions that went into the old way, often assumptions that were never explicitly identified or written down. Those who lead such change in an organization must understand that this process must go on for each person involved. It is never easy, but it is sometimes important. We should seek to do this in important areas, not in every area.


Brain Rules: 12 Principles for Surviving at Work, Home, and School by John Medina. Seattle: Pear Press, 2008.

Thinking Fast and Slow by Daniel Kahneman. New York: Farrar, Straus, and Giroux, 2011.


Al Erisman is executive editor of Ethix, which he co-founded in 1998.
He spent 32 years at The Boeing Company, the last 11 as director of technology.
He was selected as a senior technical fellow of The Boeing Company in 1990,
and received his Ph.D. in applied mathematics from Iowa State University.