A team of Princeton researchers has conducted the first study of how baby and adult brains interact during natural play, and they found measurable similarities in their neural activity. In other words, baby and adult brain activity rose and fell together as they shared toys and eye contact. The research was conducted at the Princeton Baby Lab, where University researchers study how babies learn to see, talk and understand the world.

“Previous research has shown that adults’ brains sync up when they watch movies and listen to stories, but little is known about how this ‘neural synchrony’ develops in the first years of life,” said Elise Piazza, an associate research scholar in the Princeton Neuroscience Institute (PNI) and the first author on a paper published Dec. 17, 2019, in Psychological Science.

Piazza and her co-authors — Liat Hasenfratz, an associate research scholar in PNI; Uri Hasson, a professor of psychology and neuroscience; and Casey Lew-Williams, an associate professor of psychology — posited that neural synchrony has important implications for social development and language learning.

Studying real-life, face-to-face communication between babies and adults is quite difficult. Most past studies of neural coupling, many of which were conducted in Hasson’s lab, involved scanning adults’ brains with functional magnetic resonance imaging (fMRI), in separate sessions, while the adults lay down and watched movies or listened to stories.

But to study real-time communication, the researchers needed to create a child-friendly method of recording brain activity simultaneously from baby and adult brains. With funding from the Eric and Wendy Schmidt Transformative Technology Grant, the researchers developed a new dual-brain neuroimaging system that uses functional near-infrared spectroscopy (fNIRS), which is highly safe and records oxygenation in the blood as a proxy for neural activity. The setup allowed the researchers to record the neural coordination between babies and an adult while they played with toys, sang songs and read a book.

The same adult interacted with all 42 infants and toddlers who participated in the study. Of those, 21 had to be excluded because they “squirmed excessively,” and three others flat-out refused to wear the cap, leaving 18 children, ranging in age from 9 months to 15 months.

The experiment had two portions. In one, the adult experimenter spent five minutes interacting directly with a child — playing with toys, singing nursery rhymes or reading Goodnight Moon — while the child sat on their parent’s lap. In the other, the experimenter turned to the side and told a story to another adult while the child played quietly with their parent.

The caps collected data from 57 channels of the brain known to be involved in prediction, language processing and understanding other people’s perspectives.

When they looked at the data, the researchers found that during the face-to-face sessions, the babies’ brains were synchronized with the adult’s brain in several areas known to be involved in high-level understanding of the world — perhaps helping the children decode the overall meaning of a story or analyze the motives of the adult reading to them.

When the adult and infant were turned away from each other and engaging with other people, the coupling between them disappeared.

That fit with researchers’ expectations, but the data also had surprises in store. For example, the strongest coupling occurred in the prefrontal cortex, which is involved in learning, planning and executive functioning and was previously thought to be quite underdeveloped during infancy.

“We were also surprised to find that the infant brain was often ‘leading’ the adult brain by a few seconds, suggesting that babies do not just passively receive input but may guide adults toward the next thing they’re going to focus on: which toy to pick up, which words to say,” said Lew-Williams, who is a co-director of the Princeton Baby Lab.

“While communicating, the adult and child seem to form a feedback loop,” Piazza added. “That is, the adult’s brain seemed to predict when the infants would smile, the infants’ brains anticipated when the adult would use more ‘baby talk,’ and both brains tracked joint eye contact and joint attention to toys. So, when a baby and adult play together, their brains influence each other in dynamic ways.”

This two-brain approach to neuroscience could open doors to understanding how coupling with caregivers breaks down in atypical development — such as in children diagnosed with autism — as well as how educators can optimize their teaching approaches to accommodate children’s diverse brains.

The researchers are continuing to investigate how this neural coupling relates to preschoolers’ early language learning.

A paper describing the research by scientists at Penn State and the University of California, Santa Barbara appears online in the journal Ecological Monographs.

“Coral reefs are incredibly beautiful and intrinsically valuable, but they also protect thousands of miles of coastline and provide habitat for astonishing biodiversity that is the major source of protein for nearly a billion people worldwide,” said Andrew A. Shantz, Eberly Research Postdoctoral Fellow at Penn State and the first author of the paper. “Understanding how fishing impacts coral ecosystems will help us to protect this invaluable resource.”

The researchers used survey data on parrotfish populations from 282 sites across the Caribbean and compared these sites to protected reefs in the Florida Keys to assess how parrotfish populations are impacted by fishing. Surprisingly, the biomass — the total mass of parrotfish in a region — was not impacted by fishing. Instead the distribution of fish sizes changed. Fishing selectively removes large parrotfish, but the biomass is maintained because many more small parrotfish are able to occupy the region.

“Because biomass remained basically the same in regions where there is heavy fishing, we were interested to see if different sized fish played different ecological roles,” said Shantz. “We set up an experiment where we could exclude larger fish from accessing an area to see how changes in the distribution of fish sizes impacted corals and their relationship with algae.”

The experiment involved three different enclosures with different sized openings placed on the sea floor around corals in the protected Florida Keys. One enclosure allowed access to fish of any size, the second excluded only the largest parrotfish, and the third excluded large and medium sized parrotfish.

“We found that by excluding large parrotfish, the algae grew four times faster,” said Shantz. “By excluding both large and medium parrotfish, the algae grew ten times faster. So even though fishing does not reduce the biomass of the fish, it’s the larger fish that keep the algae at bay. Unless we can develop and implement fishing strategies that maintain a healthy distribution of fish sizes — for example, a slot based system with both minimum and maximum size restrictions — the corals in these regions are at risk.”

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The system could provide a boost to object identification and speed in fields where both are critical, such as autonomous vehicles, security screening and motion sensing.

The new machine-learning approach cuts out the middleman, skipping the step of creating an image for analysis by a human and instead analyzes the pure data directly. It also jointly determines optimal hardware settings that reveal the most important data while simultaneously discovering what the most important data actually is. In a proof-of-principle study, the setup correctly identified a set of 3D numbers using tens of measurements instead of the hundreds or thousands typically required.

The results appear online on December 6 in the journal Advanced Science and are a collaboration between David R. Smith, the James B. Duke Distinguished Professor of Electrical and Computer Engineering at Duke, and Roarke Horstmeyer, assistant professor of biomedical engineering at Duke.

“Object identification schemes typically take measurements and go to all this trouble to make an image for people to look at and appreciate,” said Horstmeyer. “But that’s inefficient because the computer doesn’t need to ‘look’ at an image at all.”

“This approach circumvents that step and allows the program to capture details that an image-forming process might miss while ignoring other details of the scene that it doesn’t need,” added Aaron Diebold, a research assistant in Smith’s lab. “We’re basically trying to see the object directly from the eyes of the machine.”

In the study, the researchers use a metamaterial antenna that can sculpt a microwave wave front into many different shapes. In this case, the metamaterial is an 8×8 grid of squares, each of which contains electronic structures that allow it to be dynamically tuned to either block or transmit microwaves.

For each measurement, the intelligent sensor selects a handful of squares to let microwaves pass through. This creates a unique microwave pattern, which bounces off the object to be recognized and returns to another similar metamaterial antenna. The sensing antenna also uses a pattern of active squares to add further options to shape the reflected waves. The computer then analyzes the incoming signal and attempts to identify the object.

By repeating this process thousands of times for different variations, the machine learning algorithm eventually discovers which pieces of information are the most important as well as which settings on both the sending and receiving antennas are the best at gathering them.

“The transmitter and receiver act together and are designed together by the machine learning algorithm,” said Mohammadreza Imani, research assistant in Smith’s lab. “They are jointly designed and optimized to capture the features relevant to the task at hand.”

“If you know your task, and you know what sort of scene to expect, you may not need to capture all the information possible,” said Philipp del Hougne, a postdoctoral fellow at the Institut de Physique de Nice. “This co-design of measurement and processing allows us to make use of all the a priori knowledge that we have about the task, scene and measurement constraints to optimize the entire sensing process.”

After training, the machine learning algorithm landed on a small group of settings that could help it separate the data’s wheat from the chaff, cutting down on the number of measurements, time and computational power it needs. Instead of the hundreds or even thousands of measurements typically required by traditional microwave imaging systems, it could see the object in less than 10 measurements.

Whether or not this level of improvement would scale up to more complicated sensing applications is an open question. But the researchers are already trying to use their new concept to optimize hand-motion and gesture recognition for next-generation computer interfaces. There are plenty of other domains where improvements in microwave sensing are needed, and the small size, low cost and easy manufacturability of these types of metamaterials make them promising candidates for future devices.

“Microwaves are ideal for applications like concealed threat detection, identifying objects on the road for driverless cars or monitoring for emergencies in assisted-living facilities,” said del Hougne. “When you think about all of these applications, you need the sensing to be as quick as possible, so we hope our approach will prove useful in making these ideas reliable realities.”