From the school to the workplace, whether someone achieves their goals is determined by the mental effort they invest in their tasks. A study by Brown University scientists examined what motivates people to work harder on specific tasks and in certain situations.

Recent studies have tried to quantify motivational effects by measuring how much performance on a mentally demanding task improves when there are larger incentives at stake. The new study, published in PLOS Computational Biology, takes this research one step further by showing that the way people invest their effort in a given situation depends on whether they are incentivized to perform well or to avoid performing poorly.

The scientists designed a novel task to examine how much effort people invest under different types of incentives and how they invest that effort. They were interested in whether, as the stakes for performing a task increase, a person tries to complete that task more efficiently by placing equal emphasis on being fast and accurate, or more cautiously by placing greater emphasis on being accurate than being fast. The researchers found that people invest more effort into a task when they are trying to achieve positive outcomes and when they are trying to avoid negative outcomes, but the way they invest that effort differs in the two cases. 

“By using computational modeling, we were able to tease apart the different ways in which people could increase their effort,” said the study’s senior author Amitai Shenhav, an assistant professor of cognitive, linguistic and psychological sciences at Brown who is affiliated with the University’s Carney Institute for Brain Science. “We showed that people chose different strategies for investing their effort depending on the kinds of incentives that were at stake.” 

The researchers analyzed how the amount and type of effort a person invests differs depending on the size of the incentives at stake. They also examined if the level of effort differs depending on whether the incentives were tied to achievement or to failure. The researchers found that people favored increasing efficiency when there were larger rewards at stake for good performance, but they favored caution when larger penalties were at stake for bad performance.

“Our model was able to show that these different investment strategies are in fact the rational choices to make under these two types of incentive schemes,” Shenhav said. “In other words, people should and do work more efficiently on a task when they are trying to achieve greater rewards, and they work more cautiously when they are trying to avoid greater penalties.”

As a next step, the researchers will use neuroimaging methods to better understand how the brain transforms information about positive and negative incentives into changes in a person’s effort investment strategy, Shenhav said. They will also use their tasks and modeling approach to measure how people differ in the importance they assign to incentives for achievement versus failure, he said.

“We are exploring how these individual differences are shaped by one’s upbringing, how they develop with age, and whether they are associated with one’s risk for particular psychiatric disorders,” Shenhav said. 

This work was led by Xiamin Leng, a Ph.D. student at Brown, and co-authored by Debbie Yee, a postdoctoral fellow at Brown, and Harrison Ritz, a Ph.D. student at the University. The study sits at the intersection of research in psychology, cognitive neuroscience, computer science, economics and psychiatry. Shenhav said this study would not have been possible without the support of research awards and training fellowships through the Carney Institute, including the Daniel Cooper Graduate Student Fellowship and the training programs for interactionist cognitive neuroscience and computational psychiatry.

“These forms of synergistic interactions will continue to foster innovative research into the neural and computational mechanisms that underpin motivation and decision making,” Shenhav said.