How does a tennis player like Carlos Alcaraz decide where to run to return Novak Djokovic’s ball by just looking at the ball’s initial position? These behaviours, so common in elite athletes, are difficult to explain with current computational models, which assume that the players must continuously follow the ball with their eyes. Now, researchers of the University of Barcelona have developed a model that, by combining optical variables with environmental factors such as gravity, accurately predicts how a person will move to catch a moving object just from an initial glance. These results, published in the journal Royal Society Open Science, could have potential applications in fields such as robotics, sports training or even space exploration.
The paper addresses the outfielder problem, which refers to the baseball player who stands in the outfield to catch the ball after it is hit. It is a classic challenge in physics and the neuroscience of movement, used to explore how humans and animals predict movements in a dynamic environment and how automated systems can be designed to mimic them.
Joan López-Moliner, professor at the UB’s Faculty of Psychology and member of the Institute of Neurosciences (UBneuro), has led the research and affirms that “faced with this problem, current models are based on guiding locomotion by continuously looking at the ball, while normally the elite athlete can run towards the ball without looking at it.” “Moreover, — he adds — these models do not allow predictions of where the ball will go regarding the observer.” The initial study was part of the doctoral thesis conducted by Borja Aguado, co-author and former member of the group, who, after a stay in Darmstadt (Germany), is now a researcher at the University of Vic.
The model integrates prior knowledge of the ball’s gravity and physical size into the visual information received in real time. “The model provides live signals that indicate the predicted position of the ball’s fall and the time remaining until it arrives, considering different gravity conditions. This makes it possible to predict precisely how a player will move to catch it, from the very beginning of the flight,” describes López-Moliner, who also coordinates the Vision and Control of Action research group.
Despite the importance of gravity in anticipating trajectories, this is the first time this factor has been included in such a model. “This omission has overlooked the substantial influence that gravity exerts on the trajectory, which reflects a gap in the way existing models take into account environmental constants,” says the UB professor.
Moreover, the previous models cannot explain why humans perceive whether a ball is within reach or not to decide whether to start running. “Our model does account for this, as it indicates where the object will go regarding the player,” says the researcher.
Experiments with virtual reality
To validate the model, the researchers conducted experiments in an immersive virtual reality environment, in which each participant — wearing goggles and holding a virtual reality device in their hand — had to move to the position where they thought a virtual ball would land. The controlled environment allowed a variety of gravity and ball size conditions to be simulated, showing that the empirical trajectories, movement patterns and temporal responses matched the model’s predictions. “Our model accurately predicts the trajectories observed in the different conditions by the participants. The results underline the importance of integrating environmental constants, such as gravity, to better understand how humans interact with the world around us,” says the researcher.
Virtual training for athletes and astronauts
The new model could be a basis for the future development of various practical applications, for example in sports training. “On the one hand, since the model includes several components — such as visual information or gravity — it could be applied in training or virtual simulation platforms. In this way, the degree of sensitivity of a person — such as an elite athlete — to the different components could be seen, or they could be trained to process and use the visually relevant information to optimize performance,” says López-Moliner.
Moreover, the fact that the model can consider various types of gravity could also have applications in the aerospace sector. “The model can be applied in environments with different gravities and can potentially predict the performance with which a person — for example, the astronaut on the space station — would interact with moving objects,” says the professor.
Research with artificial neural networks
The researchers are already working on the next phase: implementing the model in artificial neural networks, computational systems that mimic the functioning of neurons in the human brain. Their goal is to compare the performance of humans and artificial networks. “This would allow us to have a clearer idea of how the computations are implemented at the neural level, as we now have a model at computational scale, but not the neural implementation in an artificial neural network. This knowledge could have clear applications in the field of robotics,” concludes López-Moliner.
