Human brains solve complex problems with simple tricks

The brain makes everyday tasks feel simple, even when they involve many moving parts. It does this by breaking big goals into smaller, bite-sized steps. That’s how someone can head out for coffee without thinking twice about elevators, crosswalks, or checkout lines.

Researchers at the Massachusetts Institute of Technology designed a study to investigate how the brain manages complex choices when perfect solutions are out of reach.

Volunteers watched a ball disappear into a four-arm maze and then tried to guess which exit it would use, guided only by two timed sounds inside the maze.

Brains use shortcuts to solve problems

Humans rarely run every possible scenario in their heads. Instead, the brain relies on two well-known strategies to simplify complex problems.

One is hierarchical reasoning, which involves breaking a puzzle into layers and solving one step at a time. The other is counterfactual reasoning, where a person imagines what might have happened if a different choice had been made.

Study senior author Mehrdad Jazayeri is a professor of brain and cognitive sciences, a member of MIT’s McGovern Institute for Brain Research, and an investigator at the Howard Hughes Medical Institute.

“What humans are capable of doing is to break down the maze into subsections, and then solve each step using relatively simple algorithms,” noted Professor Jazayeri.

“Effectively, when we don’t have the means to solve a complex problem, we manage by using simpler heuristics that get the job done.”

Problem-solving strategies of the brain

The MIT team recorded each participant’s timing accuracy first. They then built computer models that tested three possibilities: pure hierarchy, pure counterfactual thinking, or a flexible mix.

Real-world performance lined up with the flexible model. The brain seemed to favor following one branch of the maze, checking the next tone, and only sometimes backtracking if the tone felt “off.”

“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?” said Professor Jazayeri.

Memory and the choice to backtrack

Switching sides in the maze depends on memory. To reconsider an earlier turn, a person must recall the first tone and judge whether it still fits.

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

People who trust their memory more often jumped back to the alternate path. Those who sensed their recall was shaky stuck with their first guess.

“People rely on counterfactuals to the degree that it’s helpful,” Jazayeri said. “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.”

Neural networks and human behavior

The team also trained a neural network on the same task. With unlimited capacity, the model nailed every trial.

Once the researchers limited its memory and forced it to give up tracking all four paths, the network used the same two shortcuts – and the same decision rule about when to switch – that the humans showed.

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

Why the brain’s strategy matters

By pinpointing when and why the brain flips between hierarchical and counterfactual thinking, the study sheds light on real-world planning, from driving routes to business decisions.

Future work aims to trace the brain signals that trigger each switch, offering clues for both neuroscience and artificial-intelligence design.

The full study was published in the journal Nature Human Behaviour.

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