WHAT IS THIS?
Daily, snackable writings and podcasts to spur changes in thinking.
A blueprint for building a better brain by slow, consistent, daily drops of influence.
The way we think is both our greatest tool - indeed our only tool - and very often it is also our biggest leash. We are only who we think we are. Our opportunities are also limited by who other people think we are. It stands to reason that if we’d like to change who we are, we must start with an effort to change our thinking. Read more here
August 3rd, 2018
We love patterns. Our ability to recognize them and extrapolate on these correlations has given us incredible abilities to understand and create.
But with anything that we’ve inherited from time. It’s fair game to expect that it’s operating with a couple of bugs.
Music is perhaps the most obvious example of this pleasure of patterns. Music is all about patterns, and indeed this hold on a level deeper than just repeating notes and beats.
The reason why some notes sound good together and form a ‘chord’ and other random assortments of notes don’t sound good has to do with this love of patterns and our innate ability to recognize them. The different notes of a chord sound good together because when the individual resonating frequencies of each note are literally laid on top of each other visually, they match up in a bunch of places – hence a pattern, that our brain can instantly hear. It’s the recognition of that overlapping pattern that gives us pleasure.
What is more interesting is that random notes that don’t sound good together have resonating frequencies that do eventually match up! But the distance between correlated parts is far enough away that it’s harder to recognize. The correlation seems less strong, and therefore less pattern is recognized and less pleasure is produced in the brain.
It’s clear we favor high correlation and short distance between correlated parts when it comes to our pattern-recognizing software.
Could this be a problem?
What if it’s beneficial to ignore the small highly correlated pattern in favor of the bigger pattern that has correlation spread out over a lot more time?
Would our pattern-recognition software lead us astray here?
In 1967 Martin Seligman performed some interesting experiments and discovered a phenomenon he dubbed ‘Learned Helplessness”
To summarize his experiments quickly, he put some dogs in an uncomfortable situation that was designed so they had no ability to change the situation and make themselves more comfortable. He then placed the same dogs in an uncomfortable situation that they could change. The dogs did nothing. They had incorrectly learned that they had no power over their situations. Given the new situation, they did not even try. While a control group that had always had some autonomy over their uncomfortable situation readily took a chance, tried something and made their situation better.
Both groups of dogs were exercising pattern recognition in the first part of the experiment. The ‘helpless’ dogs quickly came to the conclusion that there was no correlation between their efforts and any change in their environment. In this case, no correlation IS the correlation. Where as the autonomous dogs found a correlation between their efforts and the changes in their environment.
This learned correlation then carries over into the new situation. The helpless dogs are convinced that there is no correlation between their efforts and their environment, and they do nothing.
These dogs formed a conclusion very very quickly.
Humans are prone to this exact same ‘Learned Helplessness’, and many researches believe it may be at the heart of clinical depression.
Learned helplessness occurs because we think we see a pattern and a correlation:
I do this and I fail. I do it again, I fail.
Ladies and Gentlemen, we have a pattern.
And once we recognize this pattern, the conclusion it creates is impossible to remove without counter-intuitive and difficult self-questioning. The acceptance of the conclusion then creates a negative feedback loop where acceptance of the tiny pattern then replicates the pattern, making the pattern bigger, the correlation stronger, and therefore making it seem like it’s validity is more robust.
If our effort is a foregone conclusion in this way, then the result of our effort is an inaccurate reading of our effect on reality.
Accepting the conclusion creates the illusion of further confirmation that the original pattern is relevant, when in reality there is an important distinction that should be made between an authentic pattern and which tiny authentic patterns we deem relevant.
Think of how many times a baby falls and struggles as it learns to walk. Not only is the effort impressively consistent, but more importantly, the effort is just a constant fail.
Perhaps babies are literally too unaware to realize just how epically they are failing. Ironically, it is this lack of awareness that eventually enables them to succeed.