How the brain navigates cities
We seem to be wired to calculate not the shortest path but the
'pointiest' one, facing us toward our destination as much as possible.

Date:
October 18, 2021
Source:
Massachusetts Institute of Technology
Summary:
A study suggests our brains are not optimized to calculate
the shortest possible route when navigating on foot. Instead,
pedestrians use vector- based navigation, choosing 'pointiest'
paths that point most directly toward their destination, even if
the routes are longer.



FULL STORY ========================================================================== Everyone knows the shortest distance between two points is a straight
line.

However, when you're walking along city streets, a straight line may
not be possible. How do you decide which way to go?

==========================================================================
A new MIT study suggests that our brains are actually not optimized to calculate the so-called "shortest path" when navigating on foot. Based
on a dataset of more than 14,000 people going about their daily lives,
the MIT team found that instead, pedestrians appear to choose paths
that seem to point most directly toward their destination, even if those
routes end up being longer.

They call this the "pointiest path." This strategy, known as vector-based navigation, has also been seen in studies of animals, from insects to
primates. The MIT team suggests vector-based navigation, which requires
less brainpower than actually calculating the shortest route, may have
evolved to let the brain devote more power to other tasks.

"There appears to be a tradeoff that allows computational power in our
brain to be used for other things -- 30,000 years ago, to avoid a lion,
or now, to avoid a perilious SUV," says Carlo Ratti, a professor of
urban technologies in MIT's Department of Urban Studies and Planning and director of the Senseable City Laboratory. "Vector-based navigation does
not produce the shortest path, but it's close enough to the shortest path,
and it's very simple to compute it." Ratti is the senior author of the
study, which appears today in Nature Computational Science. Christian Bongiorno, an associate professor at Universite' Paris-Saclay and a member
of MIT's Senseable City Laboratory, is the study's lead author. Joshua Tenenbaum, a professor of computational cognitive science at MIT and a
member of the Center for Brains, Minds, and Machines and the Computer
Science and Artificial Intelligence Laboratory (CSAIL), is also an author
of the paper. A preprint version of this study was posted to arXiv.org
earlier this year.

Vector-based navigation Twenty years ago, while a graduate student at
Cambridge University, Ratti walked the route between his residential
college and his departmental office nearly every day. One day, he realized
that he was actually taking two different routes -- one on to the way
to the office and a slightly different one on the way back.



========================================================================== "Surely one route was more efficient than the other, but I had
drifted into adapting two, one for each direction," Ratti says. "I was consistently inconsistent, a small but frustrating realization for a
student devoting his life to rational thinking." At the Senseable City Laboratory, one of Ratti's research interests is using large datasets from mobile devices to study how people behave in urban environments. Several
years ago, the lab acquired a dataset of anonymized GPS signals from
cell phones of pedestrians as they walked through Boston and Cambridge, Massachusetts, over a period of one year. Ratti thought that these data,
which included more than 550,000 paths taken by more than 14,000 people,
could help to answer the question of how people choose their routes when navigating a city on foot.

The research team's analysis of the data showed that instead of choosing
the shortest routes, pedestrians chose routes that were slightly longer
but minimized their angular deviation from the destination. That is,
they choose paths that allow them to more directly face their endpoint
as they start the route, even if a path that began by heading more to
the left or right might actually end up being shorter.

"Instead of calculating minimal distances, we found that the most
predictive model was not one that found the shortest path, but instead one
that tried to minimize angular displacement -- pointing directly toward
the destination as much as possible, even if traveling at larger angles
would actually be more efficient," says Paolo Santi, a principal research scientist in the Senseable City Lab and at the Italian National Research Council, and a corresponding author of the paper. "We have proposed to
call this the pointiest path." This was true for pedestrians in Boston
and Cambridge, which have a convoluted network of streets, and in San Francisco, which has a grid-style street layout.

In both cities, the researchers also observed that people tended to
choose different routes when making a round trip between two destinations,
just as Ratti did back in his graduate school days.



========================================================================== "When we make decisions based on angle to destination, the street network
will lead you to an asymmetrical path," Ratti says. "Based on thousands
of walkers, it is very clear that I am not the only one: Human beings
are not optimal navigators." Moving around in the world Studies of
animal behavior and brain activity, particularly in the hippocampus,
have also suggested that the brain's navigation strategies are based on calculating vectors. This type of navigation is very different from the computer algorithms used by your smartphone or GPS device, which can
calculate the shortest route between any two points nearly flawlessly,
based on the maps stored in their memory.

Without access to those kinds of maps, the animal brain has had to come up
with alternative strategies to navigate between locations, Tenenbaum says.

"You can't have a detailed, distance-based map downloaded into the brain,
so how else are you going to do it? The more natural thing might be use information that's more available to us from our experience," he says.

"Thinking in terms of points of reference, landmarks, and angles is a very natural way to build algorithms for mapping and navigating space based
on what you learn from your own experience moving around in the world."
"As smartphone and portable electronics increasingly couple human and artificial intelligence, it is becoming increasingly important to better understand the computational mechanisms used by our brain and how they
relate to those used by machines," Ratti says.

The research was funded by MIT Senseable City Lab Consortium; MIT's
Center for Brains, Minds, and Machines; the National Science Foundation;
the MISTI/MITOR fund; and the Compagnia di San Paolo.

========================================================================== Story Source: Materials provided by
Massachusetts_Institute_of_Technology. Note: Content may be edited for
style and length.


========================================================================== Journal Reference:
1. Bongiorno, C., Zhou, Y., Kryven, M. et al. Vector-based pedestrian
navigation in cities. Nat Comput Sci, 2021 DOI:
10.1038/s43588-021-00130- y ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/10/211018112523.htm

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