Dan Ariely is is an expert in motivation and a Ph.d. in Psychology. He’s the James B. Duke Professor of psychology and behavioral economics at Duke University and is the founder of The Center for Advanced Hindsight and also the co-founder of BEworks. Ariely’s talks on TED have been watched over 7.8 million times. He is the author of Predictably Irrational (one of my favorite books of all time. My family owns a few Dan Ariely books 😉) and The Upside of Irrationality, both of which became New York Times best sellers, as well as The Honest Truth about Dishonesty.
More info: Scroll to the bottom for more info on Where to Listen, Inquiring Minds, and where to Connect with Dan Ariely. For more breakdowns go to PodcastBreakdown.com.
We get motivated around New Year’s to make significant changes because we have a perception of new beginnings. But habits are very hard to break and our motivations slip. Change takes a long time to get into effect. It takes a long time to form a new habit, and it also takes a long time for your body to change. But humans still look for quick wins and are wired to search for causality. We are heartbroken when we don’t see results quickly, because we expect to see results as fast as we implement change.
What-The-Hell effect describes the cycle of emotions and reactions you experience when you indulge, regret what you’ve done, and then go back for more.
For example: people who go on a strict diet. The moment they violate it, they say: “what the hell, today i’m not on a dieter, let me get a milkshake and a burger.” Once you say to yourself “I’m not a dieter”, which is how you defined yourself until now, you usually don’t go back to that definition. You form a new definition for yourself: “I’m not a dieter, why should I diet?” 🤔
When we have good intentions and frame them in strict conditions, but then fail to keep up, we create implications for us as individuals that make us more likely to continue failing. The lesson is to define your criteria not so strictly. If you go very strict, you might go hard, but if you fail, it could really backfire.
One of the biggest debates between psychology and economics is whether we should reward process or outcome.
For example: a doctor rewards a diabetic patient to reduce his A1C levels. The approach says: you as the individual are best suited to figure out how you can lower your number. You can do a combination of exercise, diet, medication, etc. and figure out what’s best for yourself.
But when you are outcome driven, you try to correlate causality with your actions. When you exercise, you expect your A1C levels to go down. When you eat cake, you expect higher levels. The thing is your body doesn’t work like that, it’s a complex system with many factors, and it’s very hard or impossible for an individual to pinpoint what made changes in health.
It’s really hard to find and execute the most optimized health combination, so why not focus on things that are easy for you to control? For example: eat less, take medication and exercise. You may not have optimal results, but you’ll create a behavior that can be followed and will yield good results, rather than getting frustrated.
Yes, because we can control input, and output is statistically under our control. But when it comes to output, all kinds of things could happen. There are days when you’re not too energetic, creative or motivated, but you could still be productive on those days. For example: you could set a rule to write for at least an hour everyday no matter what (regardless of how you feel), and that will result in increased progress. But when you focus on the output and just wait for days when you feel inspired, thoughtful or focused, you don’t get much work done.
Dan is working on a weight loss study that provides participants with a scale that doesn’t show your weight. Instead it shows a congratulations message when you step on it, and sends your weight data to a database online, which is accessible with provided app.
The app doesn’t show you your weight in kg or lbs, but instead grades you on a 5 point scale: weight is the same, slightly better, much better, slightly worse, and much worse. According to Dan, these are all the levels of granularity our brains can take cause we try to attribute causality. The app also has tips on how to eat better. For example: if food is on counter, you’ll eat it; if you keep healthy items in an opaque drawer in the fridge, you’ll eat those.
Hypothesis: focusing on the underlying measurements of health instead of lbs is better for us.
For example: a woman during a menstrual cycle retain more water, which means she increases weight during that time. But that doesn’t mean she gained excess weight, it’s a healthy process that may be misread.
Quantified Self is blowing up! We have a lot of apps measure all kinds of stuff, such as steps, breathing, sleeping, etc. But not one of the products on the market is very helpful yet. For example, blood pressure monitors—our blood pressure fluctuates greatly over the day and it’s even higher for some folks when they visit the doctor ;)
Since we don’t understand randomness and variance so well, we over extrapolate when we health measurements. Getting sparse data is also a recipe for bad behavior. We need more data points. Monthly checkups could be helpful, but maybe we shouldn’t get all of our data because it creates superstition. That’s just how the brain works, it likes to associate results with causality rather than randomness (even with small samples).
Dan conducted studies to see if people will do a boring task if they find it meaningful. He conducted two studies with the goal to measure at one point would a participant have enough?
A bionicle is Lego a toy for kids 6–14, which is a movable robot.
“A bionicle is an amazing invention by LEGO.” — Dan Ariely
Structure: the study had participants build bionicles one after another for money, but with diminishing pay. Participants got paid less each time starting at $3, then $2.70, $2.40, etc.
Condition A (meaningful condition): every time a participant finished building a bionicle, the staff disassembled the bionicle and put it in a box for the next participant.
Condition B (sisyphic condition): named after Sisyphus a Greek king, who was punished by the gods to push a rock up a big hill, and every time right before he got to the end, the rock would roll down, and he would have to start again from the beginning. In this condition, after a participant finished his first bionicle assembly, the staff would ask if they want to do another, and while a participant was working on a new one, the staff disassembled the one the participant just finished. Once he was done with the second one, the staff asked if he wants to do the first one again, and that cycle would go on and on between two bionicles.
Results: people stopped building much faster in condition B, where participants were doing the same thing over and over and over. Participants felt no sense of progress, and were thinking “why am I doing this? I just did it.” Participants love for LEGO was a factor in condition A only. In condition B there was no correlation, the joy was sucked out of the situation.
Structure: the second study had a similar procedure of diminishing pay wage. Participants were given pieces of papers, where they needed to match up papers of same characters.
Condition A (acknowledge condition): participants wrote their names and gave it to a research assistant who scanned the papers and said “aha” upon receiving their papers.
Condition B (ignore condition): the research assistant didn’t look at the papers or said anything.
Condition C (shred condition): the research assistant put the papers straight into a shredder without looking at them or saying anything.
Results: people worked most in the condition A and least in C. Condition B (ignored condition) was almost as bad as condition C (shredded condition). Simply by not acknowledging people we demotivate them a lot. Just looking at something and saying “aha” has huge contribution.
We all seek efficiency, but at what cost? Lots of workplaces bombard employees with procedures, bureaucracies and meaningless steps that suck the joy out of life. We think about people as robots who can do work, without thinking about the motivation factor. In Silicon Valley companies offer tons of perks, like ping-pong tables and free beer. Perks help with employees morale to some extent, but people are looking for meaning. They want to feel commitment, appreciation and a sense of progress.
Dan says we have a funny notion of our right to pursue happiness, but what kind of happiness do we have in mind? we think mojitos on the beach and watching Seinfeld will make us happy, and we think pain is misery. But when you look what things people strive for it’s not a life on the couch watching sitcoms. People are starting companies, running marathons, etc. They are doing difficult things that are not so joyful, because we want that feeling of accomplishment. Conquering challenges, testing our ability and improving skills is what really motivates us.
Dan’s advice is think long-term: what would make you feel accomplished in a month, a year, a decade?
All Inquiring Minds episodes are free to listen on SoundCloud:
Note: timestamps are based on iTunes version.
I’ve recently discovered Inquiring Minds and love its focus on science and critical thinking. The show is hosted by two people: Indre Viskontas, a neuroscientist and operatic soprano. She holds a Ph.D. in cognitive neuroscience and a M.M. in opera and now serves on the faculty at the San Francisco Conservatory of Music. And Kishore Hari, Co-Founder of BayAreaScience, a web portal aggregating events and educational content from all the various science institutions throughout the Bay Area.
“Each week the Inquiring Minds podcast brings you a new, in-depth exploration of the place where science, politics, and society collide.“ — Mother Jones
Dan’s new book: Payoff: The Hidden Logic That Shapes Our Motivations:
This was a HELLA LONG post packed with knowledge. Hope you made it through. Did you like it? Would you want me to take notes on another podcast episode? Which one? shoot me an email at email@example.com or make your suggestion here.