There Is Such a Thing as Too Much of an Incentive

People can become so afraid of losing their potentially lucrative reward that their performance suffers

A new study by researchers at the California Institute of Technology (Caltech) suggests that when there are high financial incentives to succeed, people can become so afraid of losing their potentially lucrative reward that their performance suffers. It is a somewhat unexpected conclusion. After all, you would think that the more people are paid, the harder they will work, and the better they will do their jobs—until they reach the limits of their skills. That notion tends to hold true when the stakes are low, says Vikram Chib, a postdoctoral scholar at Caltech and lead author of a paper published in the 10 May issue of the journal Neuron.

According to a university press release, ‘In the study, each participant was asked to control a virtual object on a screen by moving an index finger that had a tracking device attached to it. After a training period, the subjects were asked to perform the task while inside an fMRI machine. The task began with the researchers offering the participants a randomised range of rewards—from $0 to $100—if they could successfully place the object into the square within the time limit. At the end of hundreds of trials—each with varying reward amounts—the participant was given the reward, based on the result of just one of the trials, picked at random. As expected, the team found that performance improved as the incentives increased—but only when the cash reward amounts were at the low end of the spectrum. Once the rewards passed a certain threshold, which depended on the individual, performance began to fall off.’

Chib says, “When people see the incentive that they’re being offered, they initially encode it as a gain. But when they’re actually doing the task, the thing that causes them to perform poorly is that they worry about losing a potential incentive they haven’t even received yet.” He adds, “We’re showing loss aversion even though there are no explicit losses anywhere in the task—that’s very strange and something you really wouldn’t expect.”

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