The human brain is incredibly skilled at solving complicated problems. One reason for this is that humans can break down complex tasks into manageable subtasks that are easy to solve one at a time. This strategy helps us handle obstacles easily, as shown by the example of going out for coffee, where we can revise how we get out of the building without changing the other steps.

While there’s a great deal of behavioral evidence demonstrating humans’ skill at these complicated tasks, it’s been difficult to devise experimental scenarios that allow precise characterization of the computational strategies used to solve problems. A new study by MIT researchers has successfully modeled how people deploy different decision-making strategies to solve a complicated task – in this case, predicting how a ball will travel through a maze when the ball is hidden from view.

The human brain cannot perform this task perfectly because it’s impossible to track all possible trajectories in parallel, but the researchers found that people can perform reasonably well by flexibly adopting two strategies known as hierarchical reasoning and counterfactual reasoning. The researchers were also able to determine the circumstances under which people choose each of those strategies.

“Weak humans are capable of doing is breaking down the maze into subsections, and then solving each step using relatively simple algorithms,” says Mehrdad Jazayeri, a professor of brain and cognitive sciences at MIT. “When we don’t have the means to solve a complex problem, we manage by using simpler heuristics that get the job done.”

The researchers recruited about 150 human volunteers to participate in the study and evaluated how accurately they could estimate timespans of several hundred milliseconds. For each participant, the researchers created computational models that could predict the patterns of errors that would be seen for that participant if they were running parallel simulations, using hierarchical reasoning alone, counterfactual reasoning alone, or combinations of the two reasoning strategies.

The researchers compared the subjects’ performance with the models’ predictions and found that for every subject, their performance was most closely associated with a model that used hierarchical reasoning but sometimes switched to counterfactual reasoning. This suggests that instead of tracking all possible paths that the ball could take, people broke up the task into smaller subtasks, picked the direction in which they thought the ball turned at the first junction, and continued to track the ball as it headed for the next turn.

If the timing of the next sound they heard wasn’t compatible with the path they had chosen, they would go back and revise their first prediction – but only some of the time. Switching back to the other side represents a shift to counterfactual reasoning, which requires people to review their memory of the tones that they heard.

The researchers found that people decided whether to go back or not based on how good they believed their memory to be. “People rely on counterfactuals to the degree that it’s helpful,” Jazayeri says. “People who take a big performance loss when they do counterfactuals avoid doing them. But if you’re someone who’s really good at retrieving information from the recent past, you may go back to the other side.”

The research was funded by various organizations, including the Lisa K. Yang ICoN Fellowship, the Friends of the McGovern Institute Student Fellowship, and the National Science Foundation Graduate Research Fellowship.

By slightly varying the amount of memory impairment programmed into the models, the researchers also saw hints that the switching of strategies appears to happen gradually, rather than at a distinct cut-off point. They are now performing further studies to try to determine what is happening in the brain as these shifts in strategy occur.

The way we display product ratings has a profound impact on consumer behavior. A new marketing study from the Cornell SC Johnson College of Business reveals that consumers tend to overestimate fractional star ratings while underestimating numeric ratings. This can lead companies to unknowingly overpromise or underdeliver, potentially affecting their sales and reputation.

In six experiments, researchers found consistent results across all participants, demonstrating a clear difference in perception when ratings are displayed using stars versus numbers. The study’s first author, Deepak Sirwani, explained that the brain automatically completes half-pictures when presented with fractional star ratings, making them feel more like 3s than 4s. On the other hand, numeric ratings cause participants to focus on the left digit, resulting in an underestimation of their magnitude.

This phenomenon is not just about numbers; it’s also about how our brains process images and standard Arabic numerals. According to Manoj Thomas, a professor of management at Cornell University, “Our results suggest that the brain representations activated when processing stars are completely different from those activated when processing Arabic numerals.” This fundamental difference in brain processing has significant implications for businesses, as it can impact consumer perceptions and sales.

The researchers’ findings have far-reaching consequences, highlighting the need for new industry standards in rating formats. As Sirwani pointed out, ratings have become a crucial factor in purchasing decisions, often rivaling price, brand reputation, or even personal recommendations from friends and family. In fact, other research has shown that even a 0.2-point increase in ratings can boost sales by up to 300%. Given this context, the study’s conclusion that the star effect can potentially increase sales by orders of magnitude is both striking and significant.

In conclusion, the way we display product ratings matters more than ever. Businesses must be aware of the “star effect” and its potential impact on consumer perceptions, taking steps to ensure their rating systems accurately reflect the quality of their products or services. Only then can companies confidently promise what they deliver, ultimately leading to increased customer satisfaction, loyalty, and growth.

The Flexible Value of Data Privacy: A New Perspective on Protecting Personal Information

In our increasingly networked world, questions about data privacy have become a ubiquitous concern for companies, policymakers, and the public. A recent study by MIT researchers has shed new light on this issue, suggesting that people’s views about privacy are not fixed and can shift significantly based on different circumstances and uses of data.

“There is no absolute value in privacy,” says Fabio Duarte, principal research scientist at MIT’s Senseable City Lab. “Depending on the application, people might feel use of their data is more or less invasive.”

The study, which used a game called Data Slots to elicit public valuations of data privacy, found that values attributed to data are combinatorial, situational, transactional, and contextual. The researchers created a card game with poker-type chips that allowed players to hold hands of cards representing various types of data, such as personal profiles, health data, vehicle location information, and more.

Players then exchanged cards, generated ideas for data uses, assessed, and invested in some of those concepts. The game was played in-person in 18 countries and online by people from another 74 countries, with over 2,000 individual player-rounds included in the study.

The results showed that players highly valued personal mobility data, followed by health data and utility use. However, the value of privacy is highly contingent on specific use-cases. For instance, players were less concerned about data privacy when using environmental data in the workplace to improve wellness.

“We show that even in terms of health data in work spaces, if they are used in an aggregated way to improve the workspace, for some people it’s worth combining personal health data with environmental data,” says Simone Mora, a research scientist at Senseable City Lab.

Martina Mazzarello adds, “Now perhaps the company can make some interventions to improve overall health. It might be invasive, but you might get some benefits back.”

The researchers suggest that taking a more flexible and user-driven approach to understanding what people think about data privacy can help inform better data policy. Cities often face scenarios where they collect aggregate traffic data, for instance. Public input can help determine how anonymized such data should be.

Understanding public opinion along with the benefits of data use can produce viable policies for local officials to pursue. “The bottom line is that if cities disclose what they plan to do with data, and if they involve resident stakeholders to come up with their own ideas about what they could do, that would be beneficial to us,” says Duarte.

“And in those scenarios, people’s privacy concerns start to decrease a lot.”