Abstract
Coordinated movements and collective decision-making in fish schools result from complex interactions by which individual integrate information about the behavior of their neighbors. However, little is known about how individuals integrate this information to take decisions and control their movements. Here, we combine experiments with computational and robotic approaches to investigate the impact of different strategies for a fish to interact with its neighbors on collective swimming in groups of rummy-nose tetra (Hemigrammus rhodostomus). By means of a data-based model describing the interactions between pairs of H. rhodostomus (Calovi et al., 2018), we show that the simple addition of the pairwise interactions with two neighbors quantitatively reproduces the collective behaviors observed in groups of five fish. Increasing the number of neighbors with which a fish interacts does not significantly improve the simulation results. Remarkably, we found groups remain cohesive even when each fish only interacts with only one of its neighbors: the one that has the strongest contribution to its heading variation. But group cohesion is lost when each fish only interact with its nearest neighbor. We then investigated with a robotic platform the impact of the physical embodiment of the interaction rules and the combinations of pairwise interactions on collective motion in groups of robots. Like fish, robots experience strong physical constraints such as the need to control their speed to avoid collisions with obstacles or other robots. We find swarms of robots are able to reproduce the behavioral patterns observed in groups of five fish when each robot interacts only with the neighbor having the strongest effect on its heading variation, and increasing the number of interacting neighbors doesn’t significantly improve the quality of group behavior. Overall, our results suggest that fish have to acquire only a minimal amount of information about their environment to coordinate their movements when swimming in groups.
Author Summary How do fish combine and integrate information from multiple neighbors when swimming in a school? What is the minimum amount of information needed by fish about their environment to coordinate their motion? To answer these questions, we combine experiments with computational and robotic modeling to test several hypotheses about how individual fish could combine and integrate the information on the behavior of their neighbors when swimming in groups. Our research shows that, for both simulated agents and robots, using the information of two neighbors is sufficient to qualitatively reproduce the collective motion patterns observed in groups of fish. Remarkably, our results also show that it is possible to obtain group cohesion and coherent collective motion over long periods of time even when individuals only interact with their most influential neighbor, that is, the one that exerts the most important force on their heading variation.