Why do people not observe?

In my last article, I discussed the blocks in learning from experience. As mentioned in Kolb’s learning cycle, even if we go through an experience but do not observe and analyze it, we are not going to learn anything from it. If we want to analyze, we will need to first observe, and then reflect. As I discussed, one does not need to go through every experience, but rather, observe or find out from someone who has learnt from experience and then go forward. When a professor shares a case study in class, or when people come together and discuss about “what happened when…” they are starting a learning cycle that begins with observation. That brings up the question – ‘Why don’t we observe or absorb properly?’ Why is that we don’t learn (for that matter, even gather knowledge) by such observation?

There are probably many reasons. Let me share some that I observed within myself and in others. One reason is that I don’t observe to record, but I observe to prove myself right. We try to collect data that is going to align with the postulates in our mind. Let me share a hypothetical case. In an office meeting, a few members are discussing how to tackle a problem that is bothering the department. Everyone talks about the issue from their perspective and spells out many challenges. Finally, it is the boss’s turn. The boss says “It is exactly as all of you are saying – the issue is not with our department at all. The issue is external. I feel ….”  This has everyone wondering “Now, when did I say what he is claiming?” No one had said the same but the boss wanted to hear what he thinks was right. As a result, the boss simply refused to accept any other data. (‘Bosses are like that.’ Most of us have felt so sometime or the other, right?) This demonstrates that we selectively pick our data. While we pick, we actually are not picking at all because we ‘do not like’ it.  The doctor says, “Your wife’s cancer is terminal.” But you hear “Her cancer is germinal,” thinking that it is due to some germs, and you hope that those germs can be killed. It is not because you have hearing problems, but you genuinely wish that somehow your wife will get cured.

The data that we choose also depends on how you approach most situations. Just think about it. You are walking on the street and an old man falls down right in front of you. Once the initial reaction of shock or support is over, what is it that you are going to naturally observe? Maybe you will try to observe the road to see if the place was slippery. You want to find out why or how. Alternatively, you may observe what is wrong with the person – is he in pain? Maybe you are passionate and helpful in nature. You observe the age of the person, or the bags he is carrying. And some of you will not observe anything and just walk away, completely forgetting that such an incident ever happened.

While we select the data, we develop filters that may be influenced by the objective. But what we state as the objective, and our inner motive or objective can be very different. I often teach using business simulations. It is interesting to see how different participants look at the same data. The objective of the simulation is quite clear - ‘to maximize profits in various rounds.’ Thus, the expectation is that the data will be used similarly by all participants. But, in reality, some participants just look at the data that they understand. A person from production looks at only the production data and not the market data. Some participants are more worried about the calculation accuracy of the report, trying to crosscheck between reports. Some participants take it on themselves to come up with suggestion on improvement in layout, and some of them are more worried about the reports of other teams. The header row of the Excel sheet is often not looked at, at all. The data that needs to be observed as part of the action is often ignored. They commit a data entry mistake, and later, when I ask them if they also face such an issue when they submit a report or fill a tender, most often, they agree that it is so!

There is another interesting aspect of the data. All of us grew up listening to stories. The case studies that are taught in the classroom are also stories. These stories go through double or more filters. So first, the storyteller filters the data based on his/her objectives, perceptions etc. and then the reader of the story filters the same. And while this Chinese-whispers-like process continues, the data is not only filtered but also distorted at each level. Is such distortion wrong? Maybe, maybe not.

For many years, the roadside mechanic learnt to service a car or a truck by observing his Ustaad.  A disciple of a classical singer who is sitting behind on the tanpura is supposed to observe the master and learn from it. A company appoints a young ‘high potential’ manager as Executive Assistant to the MD. It is expected that by shadowing the MD, this EA will observe and learn. Let us assume that all these pupils are very keen observers and they are going through the experience with the right attitude and the right objective. However, the assumption is that the master will throw open all the opportunities to observe and learn. The expectation is that the master is able to stimulate this observation and provide inputs through discussions.

Once the data that we select is observed, what we do with it is influenced by many internal and external influences. The concept of ladder of inference developed by Chris Argyris in 'Overcoming Organizational Defenses: Facilitating Organizational Learning' in 1990, describes this process beautifully. Let us assume that a young engineer is visiting a client site for some troubleshooting. He will observe many details at the site that are relevant for troubleshooting. The client’s engineer makes him wait for an hour to get approval of X. As a result, he interprets that “Unless Mr. X approves, nothing can get done.” He remembers the conversation with his colleagues that you need a lot of permissions to open the machine in this company. Thus, he concludes, ‘Mr. X has some problems in approving my request.’ He forms a belief that going to the site for troubleshooting is a ‘pain in the neck’ and a ‘waste of time’. Consequently, the next time when such a request comes from this or any other client, he flatly refuses. Most of us would agree that, for a young engineer, such experience is a very rich learning experience – not only technical learning but also behavioral insights are developed by such experience. However, this engineer has climbed the ladder of inference so fast that he has blocked all opportunities for learning.

Once the data is collected, we then have to analyze it and generalize it. In other words, we form a theory. When many people propose a theory, which is verifiable by others, it becomes an ‘accepted generalization’. Sometimes, learning will start from that generalization. In science, we often start with such a theory but often, over time, more data emerges which contradicts this theory. Therefore, the data needs to be analyzed again for refining the theory or forming a new theory. In the coming article, I will try to explore why people do not analyze data properly and form the right theory.

How do you like these articles? I am keen to know. Please share your thoughts about learning.