Are you constantly underestimating yourself?

Are you constantly underestimating yourself?


By: Cannon Thomas

One of the most consistent findings in that field has been that the vast majority of mental processing is automatic

Whether it is Steve Jobs building the iPhone, Bob Dylan giving voice to a generation, John Lassiter creating Toy Story, Mother Teresa giving the untouchables a sense of being precious and loved, or a more personal and less famous hero, all of us have had our breath taken away by extraordinary human accomplishment. But our own lives usually feel more mundane. We find roles that seem interesting and do good work, but the possibilities feel more limited. We end up calling people who accomplish extraordinary things geniuses and saints, different in kind from the rest of us.But what if the dichotomy is false? What if we are much more capable than we realise of stretching the world in new ways – ways that we cannot even see ourselves yet? Steve Jobs viewed the world this way, and he believed that understanding it can change our lives. In an interview with PBS, he said: “When you grow up you tend to get told the world is the way it is and your life is just to live your life inside the world. Try not to bash into the walls too much. Try to have a nice family life, have fun, save a little money. That’s a very limited life.

Life can be much broader once you discover one simple fact, and that is: Everything around you that you call life was made up by people that were no smarter than you. And you can change it, you can influence it, you can build your own things that other people can use..That’s maybe the most important thing. It’s to shake off this erroneous notion that life is there and you’re just going to live in it, versus embrace it, change it, improve it, make your mark upon it..”

If Jobs is right that world-changing creative potential is accessible to many of us, why do so many of us not bash against the walls? We all want to live the richest lives we can. His thinking here echoes that of leading cognitive scientists. One of the most consistent findings in that field has been that the vast majority of mental processing is automatic. Nobel-prize winning psychologist Daniel Kahneman summarises this research by describing two types of thought, which he calls System 1 and System 2. System 1 is automatic thought, and researchers have shown just how much of our attention, our interpretations of situations, our visceral feelings and attitudes, and even the goals we set happen without intention or awareness. System 2 is the conscious, deliberate thought we generally associate with thinking. In general, we think System 2 is in control, but it rarely intervenes after we are familiar with a situation. If System 1 creates a coherent approach, then System 2 is comfortable. As Kahneman says, System 2 is “lazy.”

Kahneman calls this situation “What You See Is All There Is.” We operate as if our automatic mental model is all that is relevant and miss the diverse possibilities beyond it. The bias affects us at very subtle levels, as well as in our broad perspectives on the world. Familiarity itself often becomes a source of spurious information. The tendency of our mind to fall into grooves has a direct effect on real world performance. So, how to reach for more in the most effective ways possible? It starts with a basic tenet: What We See Is NOT All There Is.

What this would look like for each of us, as individuals, will take further exploration. We may not know what is deeply inspiring to us. That is natural. If we have not spent time investigating possibilities that take us past the familiar, we will not easily be able to distinguish which ones could lead somewhere interesting.

Taking a new path will never flow as naturally as the life that is familiar to us and easily recognisable by the people around us. But maybe, like Jobs, we can decide that the mental grooves are not all there is and that living within a specious container is not good enough.

Cannon Thomas, Ph.D., is an assistant clinical professor at University of California, San Francisco.

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