LOS ANGELES: New information acts on the brain’s reward system in the same way as money or food, according to a study that explains why people can’t stop checking their phones, even when they are not expecting any important messages.
The research published in the journal Proceedings of the National Academy of Sciences demonstrates that the brain converts information into same common scale as it does for money.
It also lays the groundwork for unravelling the neuroscience behind how we consume information — and perhaps even digital addiction.
“To the brain, information is its own reward, above and beyond whether it’s useful,” said Ming Hsu, from University of California, Berkeley in the US.
“We were able to demonstrate for the first time the existence of a common neural code for information and money, which opens the door to a number of exciting questions about how people consume, and sometimes over-consume, information,” said Hsu.
“And just as our brains like empty calories from junk food, they can overvalue information that makes us feel good but may not be useful — what some may call idle curiosity,” Hsu said.
To understand more about the neuroscience of curiosity, the researchers scanned the brains of people while they played a gambling game.
Each participant was presented with a series of lotteries and needed to decide how much they were willing to pay to find out more about the odds of winning.
In some lotteries, the information was valuable — for example, when what seemed like a longshot was revealed to be a sure thing. In other cases, the information wasn’t worth much, such as when little was at stake.
For the most part, the study subjects made rational choices based on the economic value of the information (ie how much money it could help them win).
However, that did not explain all their choices: People tended to over-value information in general, and particularly in higher-valued lotteries.
It appeared that the higher stakes increased people’s curiosity in the information, even when the information had no effect on their decisions.
The researchers determined that this behaviour could only be explained by a model that captured both economic and psychological motives for seeking information.
People acquired information based not only on its actual benefit, but also on the anticipation of its benefit, whether or not it had use.
Hsu said that is akin to wanting to know whether we received a great job offer, even if we have no intention of taking it.
“Anticipation serves to amplify how good or bad something seems, and the anticipation of a more pleasurable reward makes the information appear even more valuable,” he said.
Analysing the fMRI scans, the researchers found that the information about the games’ odds activated the regions of the brain specifically known to be involved in valuation which are the same dopamine-producing reward areas of the brain activated by food, money, and many drugs.
This was the case whether the information was useful, and changed the person’s original decision, or not.
While the research does not directly address overconsumption of digital information, the fact that information engages the brain’s reward system is a necessary condition for the addiction cycle, he said.
It explains why we find those alerts saying we’ve been tagged in a photo so irresistible.
“The way our brains respond to the anticipation of a pleasurable reward is an important reason why people are susceptible to clickbait,” Hsu said.
“Just like junk food, this might be a situation where previously adaptive mechanisms get exploited now that we have unprecedented access to novel curiosities,” he added. (AGENCIES)
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SCIENCE-ROBOT-CLUTTER
New algorithms may help robots spot objects amid clutter
BOSTON, June 23:
MIT scientists have developed a technique that enables robots to quickly identify objects hidden in a 3D cloud of data.
Robots typically “see” their environment through sensors that collect and translate a visual scene into a matrix of dots.
Conventional techniques that try to pick out objects from such clouds of dots, or point clouds, can do so with either speed or accuracy, but not both.
With the new technique, a robot can accurately pick out an object, such as a small animal, that is otherwise obscured within a dense cloud of dots, within seconds of receiving the visual data.
The team said the technique can be used to improve a host of situations in which machine perception must be both speedy and accurate, including driverless cars and robotic assistants in the factory and the home.
“The surprising thing about this work is, if I ask you to find a bunny in this cloud of thousands of points, there’s no way you could do that,” said Luca Carlone, an assistant professor at Massachusetts Institute of Technology (MIT).
“But our algorithm is able to see the object through all this clutter. So we’re getting to a level of superhuman performance in localising objects,” said Carlone.
With their approach, the team was able to quickly and accurately identify three different objects — a bunny, a dragon, and a Buddha — hidden in point clouds of increasing density.
They were also able to identify objects in real-life scenes, including a living room, in which the algorithm quickly was able to spot a cereal box and a baseball hat.
The approach is able to work in “polynomial time,” it can be easily scaled up to analyze even denser point clouds, resembling the complexity of sensor data for driverless cars, for example.
“Navigation, collaborative manufacturing, domestic robots, search and rescue, and self-driving cars is where we hope to make an impact,” Carlone said. (AGENCIES)
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