Research from (Massachusetts Institute of Technology) seems to suggest that our brains aren’t the most effective navigation tools out there. According to the findings, people navigating cities tend not to follow as straight a trajectory as possible, which would be the shortest path, but tend to take the one that points most towards their destination — even if they end up walking a longer distance.
The team calls this the “pointiest path” approach. In technical terms, it is known as vector-based navigation. Animals, from the simplest to the most complex, have also shown in various experiments that they employ the same strategy. The authors believe that animal brains evolved to use vector-based navigation because, even though it isn’t the most effective approach, it is much easier to implement computationally — saving time and energy.
A general direction
“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 perilous 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.”
The findings are based on a dataset comprising the routes of over 14,000 people going about their daily lives in a city environment. These records were anonymized GPS signals from pedestrians in Boston and Cambridge, Massachusetts, and San Francisco, California, over a period of one year. All in all, they include over 550,000 paths.
The overwhelming majority of people didn’t use the shortest routes, judging from where they left and their destination. However, they did pick routes that minimized their angular derivation from the destination — they chose the routes that pointed towards where they were going the most.
“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.”
Pedestrians employed this navigation strategy both in Boston and Cambridge, which have a convoluted street layout, as well as in San Francisco, which has a highly organized, grid-style layout. In both cases, the team notes that pedestrians also tend to follow different routes when making a round trip between two points. Ratti explains that such an outcome would be expected if pedestrians made “decisions based on angle to destination” instead of judging distances only.
“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 useful information that’s more available to us from our experience,” Tenenbaum 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.”
While definitely fun, such findings may seem a bit inconsequential. The authors however believe that as we come to rely more heavily on computers such as our smartphones for everyday tasks, it is more important than ever to understand the way our own brains compute the world around us. This would allow us to design better software and improve our quality of life by tailoring our devices around the way our minds and brains work.
The paper “Vector-based pedestrian navigation in cities” has been published in the journal Nature Computational Science.
