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