Friday, June 13, 2025

How the brain solves complicated problems

Study shows humans flexibly deploy different reasoning strategies to tackle challenging mental tasks — offering insights for building machines that think more like us

Massachusetts Institute of Technology

CAMBRIDGE, MA -- The human brain is very good at solving complicated problems. One reason for that is that humans can break problems apart into manageable subtasks that are easy to solve one at a time.

This allows us to complete a daily task like going out for coffee by breaking it into steps: getting out of our office building, navigating to the coffee shop, and once there, obtaining the coffee. This strategy helps us to handle obstacles easily. For example, if the elevator is broken, we can revise how we get out of the building without changing the other steps.

While there is a great deal of behavioral evidence demonstrating humans’ skill at these complicated tasks, it has been difficult to devise experimental scenarios that allow precise characterization of the computational strategies we use to solve problems.

In a new study, MIT researchers have 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 is impossible to track all of the 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.

“What humans are capable of doing is to break down the maze into subsections, and then solve each step using relatively simple algorithms. Effectively, when we don’t have the means to solve a complex problem, we manage by using simpler heuristics that get the job done,” says Mehrdad Jazayeri, a professor of brain and cognitive sciences, a member of MIT’s McGovern Institute for Brain Research, an investigator at the Howard Hughes Medical Institute, and the senior author of the study.

Mahdi Ramadan PhD ’24 and graduate student Cheng Tang are the lead authors of the paper, which appears today in Nature Human Behavior. Nicholas Watters PhD ’25 is also a co-author.

Rational strategies

When humans perform simple tasks that have a clear correct answer, such as categorizing objects, they perform extremely well. When tasks become more complex, such as planning a trip to your favorite cafe, there may no longer be one clearly superior answer. And, at each step, there are many things that could go wrong. In these cases, humans are very good at working out a solution that will get the task done, even though it may not be the optimal solution.

Those solutions often involve problem-solving shortcuts, or heuristics. Two prominent heuristics humans commonly rely on are hierarchical and counterfactual reasoning. Hierarchical reasoning is the process of breaking down a problem into layers, starting from the general and proceeding toward specifics. Counterfactual reasoning involves imagining what would have happened if you had made a different choice. While these strategies are well-known, scientists don’t know much about how the brain decides which one to use in a given situation.

“This is really a big question in cognitive science: How do we problem-solve in a suboptimal way, by coming up with clever heuristics that we chain together in a way that ends up getting us closer and closer until we solve the problem?” Jazayeri says.

To overcome this, Jazayeri and his colleagues devised a task that is just complex enough to require these strategies, yet simple enough that the outcomes and the calculations that go into them can be measured.

The task requires participants to predict the path of a ball as it moves through four possible trajectories in a maze. Once the ball enters the maze, people cannot see which path it travels. At two junctions in the maze, they hear an auditory cue when the ball reaches that point. Predicting the ball’s path is a task that is impossible for humans to solve with perfect accuracy.

“It requires four parallel simulations in your mind, and no human can do that. It’s analogous to having four conversations at a time,” Jazayeri says. “The task allows us to tap into this set of algorithms that the humans use, because you just can’t solve it optimally.”

The researchers recruited about 150 human volunteers to participate in the study. Before each subject began the ball-tracking task, the researchers evaluated how accurately they could estimate timespans of several hundred milliseconds, about the length of time it takes the ball to travel along one arm of the maze.

For each participant, the researchers created computational models that could predict the patterns of errors that would be seen for that participant (based on their timing skill) 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.

That suggests that instead of tracking all the possible paths that the ball could take, people broke up the task. First, they picked the direction (left or right), 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, which represents a shift to counterfactual reasoning, requires people to review their memory of the tones that they heard. However, it turns out that these memories are not always reliable, and 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 are someone who’s really good at retrieving information from the recent past, you may go back to the other side.”

Human limitations

To further validate their results, the researchers created a machine-learning neural network and trained it to complete the task. A machine-learning model trained on this task will track the ball’s path accurately and make the correct prediction every time, unless the researchers impose limitations on its performance.

When the researchers added cognitive limitations similar to those faced by humans, they found that the model altered its strategies. When they eliminated the model’s ability to follow all possible trajectories, it began to employ hierarchical and counterfactual strategies like humans do. If the researchers reduced the model’s memory recall ability, it began to switch to hierarchical only if it thought its recall would be good enough to get the right answer — just as humans do.

“What we found is that networks mimic human behavior when we impose on them those computational constraints that we found in human behavior,” Jazayeri says. “This is really saying that humans are acting rationally under the constraints that they have to function under.”

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.

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The research was funded by a Lisa K. Yang ICoN Fellowship, a Friends of the McGovern Institute Student Fellowship, a National Science Foundation Graduate Research Fellowship, the Simons Foundation, the Howard Hughes Medical Institute, and the McGovern Institute.

Journal

Nature Human Behaviour

DOI

10.1038/s41562-025-02232-3

Article Title

'Computational basis of hierarchical and counterfactual information processing'

Article Publication Date

11-Jun-2025

From plastic waste to clean hydrogen: A scalable solar-powered solution

Korean researchers develop eco-friendly technology to turn plastic into hydrogen using sunlight

Institute for Basic Science

Figure 1. Turning Plastic Waste into Clean Hydrogen with Sunlight — image:
This illustration shows how a newly developed floatable nanocomposite system produces hydrogen gas by using sunlight to break down everyday plastic waste. The sponge-like material floats on water and absorbs sunlight while converting discarded PET bottles and PLA cups into useful byproducts like ethylene glycol, terephthalic acid, and lactic acid. At the same time, it releases clean hydrogen gas into the air. This simple yet powerful process demonstrates a sustainable and scalable way to upcycle plastic waste into renewable energy using only natural sunlight and water.
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Credit: Institute for Basic Science

A team of Korean scientists has developed an innovative green technology that transforms plastic waste into clean hydrogen fuel using only sunlight and water.

Researchers at the Institute for Basic Science (IBS) Center for Nanoparticle Research, led by Professor KIM Dae-Hyeong and Professor HYEON Taeghwan of Seoul National University, announced the successful development of a photocatalytic system that produces hydrogen from PET bottles. The key innovation lies in wrapping the photocatalyst in a hydrogel polymer, which helps it float on water and stay active even under harsh environmental conditions.

Hydrogen is gaining attention as a next generation clean energy source. However, the most common method for producing it—methane steam reforming—consumes large amounts of energy and releases significant greenhouse gas emissions. Photocatalytic hydrogen production, which relies on sunlight, is a cleaner alternative but faces challenges in maintaining stability under strong light and chemical stress.

To overcome these limitations, the IBS research team introduced a strategy that stabilizes the catalyst within a polymer network while placing the reaction site at the interface between air and water. This setup allows the system to avoid common problems such as catalyst loss, poor gas separation, and reverse reactions. The system breaks down plastics like PET into useful byproducts such as ethylene glycol and terephthalic acid, while releasing clean hydrogen into the air.

“The key was engineering a structure that works not only in theory but also under practical outdoor conditions,” explained Dr. LEE Wanghee, a postdoctoral researcher at MIT and co-first author of the study. “Every detail — from material design to the water-air interface — had to be optimized for real-life usability.”

The researchers demonstrated that their system remained stable for over two months, even in highly alkaline conditions. The floatable catalyst system also works in diverse real-world water environments, including seawater and tap water.

In tests using a one-square-meter device placed outdoors under natural sunlight, the system successfully produced hydrogen from dissolved PET bottle waste. Additional economic and scale-up simulations showed that the technology can be expanded to 10 or even 100 square meters, offering a pathway toward cost-effective, carbon-free hydrogen production.

“This research opens a new path where plastic waste becomes a valuable energy source,” said Professor KIM Dae-Hyeong. “It’s a meaningful step that tackles both environmental pollution and clean energy demand.”

Professor HYEON Taeghwan added, “This work is a rare example of a photocatalytic system that functions reliably in the real world — not just the lab. It could become a key stepping stone toward a hydrogen-powered, carbon-neutral society.”

This figure shows how a one-square-meter outdoor system can turn plastic waste into hydrogen using sunlight.
(a) displays the actual setup of the steel reactor, where a sponge-like catalyst material (Pt-DSA/TiO₂ nanocomposites) floats in a plastic waste solution. Key components such as the nitrogen gas line, gear pump, and gas chromatograph used to measure hydrogen are also shown.
(b) provides a top-down schematic view of the reactor, which includes four quartz windows that let sunlight in.
(c) tracks the system’s performance over two separate days, showing a steady rise in hydrogen production (green) as sunlight intensity (orange) increases throughout the day. Even with natural temperature changes (blue), the system maintained high efficiency, proving it can reliably produce hydrogen from waste in outdoor conditions.
Credit
Institute for Basic Science