"Everything's Amazing, but Nobody's Happy"

During his now famous 2011 appearance on "The Late Show With Conan O'Brien", comedian Louis CK delivered a rant now commonly known as “Everything's amazing, but nobody's happy.", about the irony of perceived first-world hardships and our propensity to quickly focus on even the smallest drawbacks in a life otherwise filled with technological miracles.

The Atlantic's Julie Beck interviewed neuropsychologist Dr. Rick Hanson on this unfortunate but all too familiar paradigm, exploring the way that we overlook the ongoing privilege of living in the convenience and safety of modern times, choosing so often to focus on anything negative we can find:

That’s why the brain today has what scientists call a negativity bias. I describe it as like Velcro for the bad, Teflon for the good. For example, negative information about someone is more memorable than positive information, which is why negative ads dominate politics. In relationships, studies show that a good, strong relationship needs at least a 5:1 ratio of positive to negative interactions.

Unfortunately, I think Dr. Hanson's observation is true for most of us. He explains further:

On the one hand, due to modernity, many people report that moment to moment, they’re having fairly positive experiences, they’re not being chased by lions, they’re not in a war zone, they’re not in agonizing pain, they have decent medical care. And yet on the other hand, many people today would report that they have a fundamental sense of feeling stressed and pressured and disconnected from other people, longing for closeness that they don’t have, frustrated, driven, etc. Why is that? I think one reason is that we’re simply wasting the positive experiences that we’re having, in part due to modernity, because we’re not taking into account that design bug in the Stone Age brain that it doesn’t learn very well.

The piece is full of great insights into the concept of happiness, our core human needs, and training our brains to overcome a negativity bias. It's worth a read.