Originally shared by Edmond Lau, Engineer at Quip on Quora (with 4.4k upvotes)
In 2009, Dr. Heidi Grant Halvorson made a surprising discovery in the science of motivation. She conducted a series of studies where she asked participants to solve a set of puzzles and problems. In one group — the “be-good” group — participants were told that their score reflected their “conceptual and analytical abilities.”
They should try to solve as many problems as possible and aim for a high score to demonstrate how good they were. In another group — the “get-better” group — participants were told that each problem was a “training tool” and that they ought to “take advantage of this valuable learning opportunity” to improve their problem-solving skills.
For some participants in each group, Halvorson also increased the difficulty level by introducing a few challenges. She interrupted participants to use up some of their allotted time. She threw in extra, unsolvable problems to frustrate them, without telling participants that the problems were unsolvable.
What surprised Halvorson was how the two groups dealt with the challenges. The ones in the “get-better” group remained unfazed and solved as many as problems in the challenging conditions as the easy ones. They stayed motivated and kept trying to learn. The ones in the “be-good” group, however, were so demoralized when they faced the challenges and obstacles that they solved substantially fewer problems than those who didn’t have to face them.
And those differences happened just because of how the initial goal was framed.
Define Mastery Goals, Not Performance Ones, For Difficult Problems
Halvorson’s experiments illustrate the difference between a mastery goal, where you aim to learn and get better at some skill, and a performance goal, where you aim to be good, either to demonstrate you’re talented or to outperform other people.
Your objective for a given problem can often be framed in either way:
1. Are you studying for tests to learn and to grow or to demonstrate your intelligence?
2. Are you spending years on a PhD to innovate in your research area or to because you think it’ll be a good stepping stone for your career?
3. Are you training for a 10K race to improve your own time or to beat the competition?
4. Are you working on side projects and brushing up your coding skills to become a better software engineer or to simply get a better-paying job?
The actions you perform to accomplish a mastery goal or a performance goal might be the same, but your motivation and your mindset will be quite different. When you’re focused on improving your own skills, rather than on demonstrating them, you’re less likely to get discouraged by obstacles, time pressure, or other unexpected challenges. You’ll believe that you can still improve and do better next time. You’ll have a.
That’s not to say performance goals don’t have their place. Professor Dan Ariely conducted a series of experiments at MIT, the University of Chicago, and in rural Madurai, India. Subjects were asked to participate in a number of games and offered either a small, moderate, or large financial incentive for performing well on each particular game — a clear example of performance goals in action.
For memory games, creativity games, or motor skill games, those offered a large financial incentive actually performed worse than those offered smaller ones. The only task where participants actually performed better when offered a large financial incentive was when they were performing the mechanical task of alternating keypresses on a keyboard as quickly as possible. 
Daniel Pink reinforces this idea in his book, explaining that when there is a clear goal and when the problem can be solved by brute force, performance-based goals — especially those incentivized by a reward — work extremely well. It’s when the problems require some ingenuity or some mental effort, that performance-based goals and rewards start to backfire and reduce performance.
Making This Research Useful
Set the right type of goal for the task at hand to get better results.
You’re better off setting a performance goal when you can brute force through the problem, particularly if there’s a reward at stake. For example, performance goals work well if you’re:
1. Triaging through a long bug or feature list.
2. Responding to a long backlog of personal emails or customer support emails.
3. Finishing a laundry list of chores around the apartment.
4. Mechanically grinding through any number of mindless tasks.
It can be helpful for each of these short-term tasks, where there isn’t much opportunity to master a new skill, to instead tie a reward to the completion of the task. If you get everything done, then you’ll treat yourself (or your team) to something nice.
The performance incentive can help you get things done faster.
But for our long-term goals, we’ll stay much more motivated in the long run if we adopt a mindset where we’re aiming to master our skills rather than to hit a performance goal. For example,
1. Rather than focusing on getting promoted to a staff engineering position at your company, focus on improving your engineering skills and your ability to create meaningful impact.
2. Rather than training to win at some sport — whether it’s running, a tennis match, ultimate frisbee, etc. — train to become a better player or athlete.
3. Rather than joining at a startup to get rich, join because you’re passionate about the problem space and excited to learn from the journey.
You’ll notice that long-term goals framed in terms of performance tend to rely on external factors outside of your control (whether your manager promotes you, whether you’re better than your opponent, or whether your startup succeeds).
When you let environmental circumstances play such a large role in your success, it’s much harder to stay motivated when you encounter obstacles, just like the puzzle-solving participants in Halvorson’s experiments. If you instead focus on your own learning and on getting better, you’re much more likely to overcome pain points and actually succeed.