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Athletes respond differently to robotic and human opponents

In a groundbreaking study conducted at the University of Florida, researchers have investigated how our brains react to high-speed sports like table tennis and how these reactions differ when playing against a human or a machine. 

The research, led by graduate student Studnicki and Professor Daniel Ferris, could provide valuable insights into sports training and the development of more naturalistic robotic companions.

The study involved participants wearing caps with electrodes, which were connected to a backpack, giving them a cyborg-like appearance. The participants played table tennis against either Studnicki or a ball-serving machine. The researchers aimed to understand the neurological reactions to the intense demands of a fast-paced sport and the difference a machine opponent makes.

Studnicki and Ferris found that the brains of table tennis players reacted very differently when playing against a human or a machine. When faced with a ball machine, players’ brains displayed desynchronization, an indication of significant mental activity and anticipation of the next serve. 

By contrast, when playing against a human opponent, their neurons worked in unison, seemingly confident of their next move. This suggests that training with a machine might not offer the same experience as playing against a real opponent.

A participant plays table tennis against graduate student Amanda Studnicki while having his brain imaged via an EEG cap.
Image Credit: Frazier Springfield

According to Ferris, understanding our brains’ response to robots could help improve the development of artificial companions. “Humans interacting with robots is going to be different than when they interact with other humans. Our long-term goal is to try to understand how the brain reacts to these differences,” he said.

The research team has long studied the brain’s response to visual cues and motor tasks. With Studnicki’s tennis background, they decided to focus on table tennis as a complex, fast-paced action sport. The researchers doubled the number of electrodes in a typical brain-scanning cap to 240, allowing them to better account for the rapid head movements during a table tennis match.

The study focused on the parieto-occipital cortex, the brain region responsible for turning sensory information into movement. “We wanted to understand how it worked for complex movements like tracking a ball in space and intercepting it, and table tennis was perfect for this,” said Studnicki.

After analyzing dozens of hours of play against both Studnicki and the ball machine, the researchers observed that when playing against another person, players’ neurons worked in unison. However, when playing against a ball-serving machine, the neurons in their brains were not aligned with one another, displaying desynchronization.

Ferris explained the importance of synchronization in the brain, likening it to a crowd cheering in unison at a football stadium. Desynchronization, on the other hand, indicates the brain is doing a lot of calculations, as opposed to idling.

Despite the difference in brain activity, Studnicki still sees value in practicing with a machine. “But I think machines are going to evolve in the next 10 or 20 years, and we could see more naturalistic behaviors for players to practice against,” she said. The team’s research could pave the way for more advanced and realistic training machines, ultimately benefiting athletes and improving human-robot interactions.

Why humans interact differently with robots

Humans interact differently with robots than with other humans due to several factors:

  1. Nonverbal cues: Human communication involves nonverbal cues, such as facial expressions, body language, and eye contact. These cues play a significant role in conveying emotions, intentions, and social information. Robots typically lack the ability to fully mimic these nonverbal cues, which can result in less natural and less intuitive interactions.
  2. Emotional connection: Humans are more likely to form emotional connections with other humans due to shared experiences, empathy, and the ability to understand each other’s emotions. While some robots have been designed to simulate emotions or respond to human emotions, the connection is generally not as deep or genuine as it is between humans.
  3. Social norms and expectations: Human interactions are governed by social norms, cultural practices, and expectations that have developed over time. When interacting with robots, these norms and expectations may not apply or may need to be adjusted, which can lead to a different type of interaction.
  4. Cognitive processes: As mentioned in the table tennis study, human brains react differently when interacting with robots compared to humans. The lack of predictability and recognizable cues from robots can lead to increased cognitive demands and a different neural response in humans.
  5. Trust and reliance: Trust plays a crucial role in human-human interactions. Humans may be more hesitant to trust robots, especially in situations where their well-being or safety is involved. This may lead to different patterns of reliance and collaboration between humans and robots compared to human-human interactions.
  6. Adaptability and learning: Humans are highly adaptable and can learn from each other through observation, imitation, and feedback. While robots can be programmed to learn and adapt, their learning capabilities are often limited compared to those of humans, which can impact the dynamics of human-robot interactions.

Overall, human-robot interactions are different from human-human interactions due to factors like nonverbal cues, emotional connections, social norms, cognitive processes, trust, and adaptability. As robotic technology advances and robots become more sophisticated, the gap between human-human and human-robot interactions may narrow, but fundamental differences are likely to remain.


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