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What is our brain's method of city navigation? Selecting the "Pointiest" Route, Not the Shortest

Everyone is aware that the straight line is the shortest distance between any two points on the earth. When walking through city streets, however, it is not always possible to walk in a straight line. What criteria do you use to determine which path to take?

We are not designed for walking, according to a new MIT study, because our brains are not optimized for calculating the "shortest path." After compiling data from more than 14,000 people going about their daily lives, the MIT researchers discovered that pedestrians prefer paths that appear to point most directly toward their destination, even if those routes are longer. The "pointiest path" is the path that has the most points.

It has been observed in a wide range of animal studies, from insects to primates, that this navigation strategy, dubbed "vector-based navigation," is used. Because it requires less brainpower than calculating the shortest route, the MIT team hypothesizes that vector-based navigation evolved in order to free up brain resources for other tasks.

According to Carlo Ratti, an associate professor of urban technologies and director of MIT's Senseable City Laboratory, "there appears to be a trade-off that allows our brain's computational power to be used for other purposes, whether it was 30,000 years ago to avoid a lion or now to avoid a potentially dangerous SUV." Although vector-based navigation does not always produce the shortest path, it is extremely simple to compute and comes close enough to the goal.

Dr. Ratti is the study's senior author, and the findings were published in the journal Nature Computational Science on October 18, 2021. Christian Bongiorno, an associate professor at Université Paris-Saclay and a member of the Massachusetts Institute of Technology's Senseable City Laboratory, is the study's lead author. Mr. Joshua Tenenbaum is an associate professor of computational cognitive science at the Massachusetts Institute of Technology (MIT). He is also a member of the Center for Brains, Minds, and Machines as well as the Computer Science and Artificial Intelligence Laboratory (CSAIL).

When Ratti was a graduate student at Cambridge University twenty years ago, he walked the route between his residential college and his departmental office nearly every day. He was driving two different routes to and from work when he realized one day that he was taking two different routes — one to the office and another slightly different route home.

According to Ratti, "clearly, one route was more efficient than the other, but I found myself adapting two routes, one for each direction," he says. The realization that "I was consistently inconsistent" was "a minor but vexing realization" for someone who was devoted to rational thought.

In her research at the Senseable City Laboratory, Ratti is interested in the use of large datasets collected from mobile devices to better understand how people behave in urban environments. Several years ago, the lab collected a dataset of anonymized GPS signals from pedestrians' cell phones as they walked through the streets of Boston and Cambridge, Massachusetts, over the course of a year. It was Ratti's reasoning that this data, which included over 550,000 paths taken by over 14,000 people, might be useful in shedding light on how people navigate cities by foot.

The data was analyzed by the research team, and it was discovered that pedestrians chose slightly longer routes in order to minimize their angular deviation from their destination. Thus, they choose routes that allow them to face their destination more directly as they begin the route, even if the route that begins more to the left or right is shorter in the end.

We discovered that the most predictive model was one that attempted to minimize angular displacement — pointing as directly as possible toward the destination in spite of the fact that traveling at greater angles would be more efficient — rather than one that calculated minimal distances. "This was the most surprising discovery," says Paolo Santi, principal research scientist at the Senseable City Lab and the Italian National Research Council, and a corresponding author of the paper. "We propose that this be referred to as the pointiest path," says the team.

In cities like Boston and Cambridge, where streets are a maze of interconnected streets, as well as in San Francisco, where streets are laid out in rows like a grid, this was true for pedestrians. The researchers also discovered that people in both cities tended to take alternate routes when traveling between two destinations, just as Ratti did while attending graduate school.

The street network, explains Ratti, will direct us in an asymmetrical direction if we make decisions based on our angle with respect to the destination. I am not alone, as evidenced by the thousands of walkers who have joined me: human beings are not the best navigators."

Aside from that, research into animal behavior and brain activity, particularly in the hippocampus, suggests that the brain's navigation strategies are based on vector calculations. Alternatively, the computer algorithms used by your smartphone or GPS device, which can almost flawlessly calculate the shortest route between any two points using the maps stored in their memory, are in stark contrast.

Tenenbaum explains that because the animal brain does not have access to these types of maps, it has had to develop alternative strategies for navigating between locations.

"Since you will not be able to download a detailed, distance-based map into your brain, how are you going to accomplish this? It is possible that using information that is more readily available to us as a result of our previous experiences will be the more natural course of action "he explains. In terms of reference points, landmarks, and angles, thinking in terms of algorithms for mapping and navigating space based on what you learn from your own movement in the world is a very natural way to develop algorithms.

Because smartphones and portable electronics are becoming more integrated with artificial intelligence, it is critical to gain a better understanding of the computational mechanisms used by our brains, as well as their relationship to those used by machines, according to Ratti.

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