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Brittle stars can learn new stuff, no brain required

"Knowing that brittle stars can learn means they're not just robotic scavengers like little Roombas cleaning up the ocean floor," says Julia Notar. "They're potentially able to expect and avoid predators or anticipate food because they're learning about their environment." (Credit: Getty Images)

Headless animals called brittle stars have no brains at all and still manage to learn through experience, new research reveals.

Relatives of starfish, brittle stars spend most of their time hiding under rocks and crevices in the ocean or burrowing in the sand.

These shy marine creatures have no brain to speak of—just nerve cords running down each of their five wiggly arms, which join to form a nerve ring near their mouth.

“There’s no processing center,” says lead author Julia Notar, who did the research as part of her biology PhD in the lab of Sönke Johnsen, a professor at Duke University.

“Each of the nerve cords can act independently,” Notar says. “It’s like instead of a boss, there’s a committee.”

In the case of brittle stars, that seems to be enough to learn by association, Notar, Johnsen, and former Duke undergraduate Madeline Go report in the journal Behavioral Ecology and Sociobiology.

This type of learning involves associating different stimuli via a process called classical conditioning. A famous example is Pavlov’s dog experiments, which showed that dogs repeatedly fed at the ringing of a bell would eventually start drooling at the mere sound of a bell, even when no food was around.

Humans do this all the time. If you hear the “ding” of a smartphone over and over again with each new alert, eventually the sound starts to have a special meaning. Just hearing someone’s phone ping or buzz with the same chime as yours is enough to make you reflexively reach for your own phone in anticipation of the next text, email, or Instagram post.

Classical conditioning has been demonstrated in a handful of previous studies in starfish. But most echinoderms—a group of some 7,000 species that includes brittle stars and similarly brainless starfish, sea urchins, and sea cucumbers—have not been tested.

To find out if brittle stars are capable of learning, the researchers put 16 black brittle stars (Ophiocoma echinata) in individual water tanks and used a video camera to record their behavior.

The researchers trained half the brittle stars by dimming the lights for 30 minutes whenever the animals were fed. Every time the lights went out, the researchers would put a morsel of shrimp—”which they love”—in the tanks, placed just out of reach.

The other half got just as much shrimp and also experienced a 30-minute dark period, but never at the same time—the animals were fed under lit conditions.

Whether it was light or dark, the animals spent most of their time hiding behind the filters in their tanks; only coming out at mealtime. But only the trained brittle stars learned to associate darkness with food.

Early in the 10-month-long experiment, the animals stayed hidden when the lights went out. But over time, the animals made such a connection between the darkness and mealtime that they reacted as if food was on its way and crept out of hiding whenever the lights went out, even before any food was put in the tanks.

These brittle stars had learned a new association: lights out meant that food was likely to show up. They didn’t need to smell or taste the shrimp to react. Just sensing the lights go dim was enough to make them come when called for dinner.

They still remembered the lesson even after a 13-day “break” without training, i.e., dimming the lights over and over again without feeding them.

The results are “exciting” because “classical conditioning hasn’t really been shown definitively in this group of animals before,” Notar says.

“Knowing that brittle stars can learn means they’re not just robotic scavengers like little Roombas cleaning up the ocean floor. They’re potentially able to expect and avoid predators or anticipate food because they’re learning about their environment.”

As a next step, Notar hopes to start to tease apart how they manage to learn and remember using a nervous system that is so different from our own.

“People ask me all the time, ‘how do they do it?'” Notar says. “We don’t know yet. But I hope to have more answers in a few years.”

The US Department of Department of Defense through the National Defense Science & Engineering Graduate Fellowship Program, the Duke Nicholas School Rachel Carson Scholars program, and the Duke department of biology funded the work.

Source: Duke University

  • Some vocal learners are really good problem solvers, too
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    ‘Brainless’ robot can navigate twisty mazes

    "We've developed a new soft robot that is capable of turning on its own, allowing it to make its way through twisty mazes, even negotiating its way around moving obstacles," says Jie Yin. "And it's all done using physical intelligence, rather than being guided by a computer." (Credit: Mitchell Luo/Unsplash)

    Researchers have created a “brainless” soft robot that can navigate complex and dynamic environments.

    The same team previously created a soft robot that could navigate simple mazes without human or computer direction.

    “In our earlier work, we demonstrated that our soft robot was able to twist and turn its way through a very simple obstacle course,” says Jie Yin, associate professor of mechanical and aerospace engineering at North Carolina State University and co-corresponding author of a study in Science Advances.

    The two-pronged robot on a black background.
    (Credit: NC State)

    “However, it was unable to turn unless it encountered an obstacle. In practical terms this meant that the robot could sometimes get stuck, bouncing back and forth between parallel obstacles.

    “We’ve developed a new soft robot that is capable of turning on its own, allowing it to make its way through twisty mazes, even negotiating its way around moving obstacles. And it’s all done using physical intelligence, rather than being guided by a computer.”

    Physical intelligence refers to dynamic objects—like soft robots—whose behavior is governed by their structural design and the materials they are made of, rather than being directed by a computer or human intervention.

    As with the earlier version, the new soft robots are made of ribbon-like liquid crystal elastomers. When the robots are placed on a surface that is at least 55 degrees Celsius (131 degrees Fahrenheit), which is hotter than the ambient air, the portion of the ribbon touching the surface contracts, while the portion of the ribbon exposed to the air does not. This induces a rolling motion; the warmer the surface, the faster the robot rolls.

    However, while the previous version of the soft robot had a symmetrical design, the new robot has two distinct halves. One half of the robot is shaped like a twisted ribbon that extends in a straight line, while the other half is shaped like a more tightly twisted ribbon that also twists around itself like a spiral staircase.

    This asymmetrical design means that one end of the robot exerts more force on the ground than the other end. Think of a plastic cup that has a mouth wider than its base. If you roll it across the table, it doesn’t roll in a straight line—it makes an arc as it travels across the table. That’s due to its asymmetrical shape.

    “The concept behind our new robot is fairly simple: because of its asymmetrical design, it turns without having to come into contact with an object,” says Yao Zhao, a postdoctoral researcher and the paper’s first author.

    “So, while it still changes directions when it does come into contact with an object—allowing it to navigate mazes—it cannot get stuck between parallel objects. Instead, its ability to move in arcs allows it to essentially wiggle its way free.”

    The researchers demonstrated the ability of the asymmetrical soft robot design to navigate more complex mazes—including mazes with moving walls—and fit through spaces narrower than its body size. The researchers tested the new robot design on both a metal surface and in sand.

    “This work is another step forward in helping us develop innovative approaches to soft robot design—particularly for applications where soft robots would be able to harvest heat energy from their environment,” Yin says.

    The National Science Foundation supported the work.

    Source: NC State