A study of locusts navigating in a novel virtual reality (VR) environment challenges traditional models of collective swarming behavior, researchers report. The findings show that the insects don’t just follow their neighbors like self-propelled particles but instead rely on internal cognitive decision-making processes to navigate as a collective. Collective motion, a phenomenon found widely in nature, has traditionally been described using "self-propelled particle" theoretical models from physics. These “classical” models of collective behavior, like the highly influential Vicsek model, explain how basic local interactions, where individuals align and synchronize their movements with those of nearby members, can give rise to large-scale, coordinated motion within groups. Desert locust (Schistocerca gregaria) swarms, which can cover vast areas and contain billions of individuals, serve as a prominent example of collective motion in nature. Using a combination of field experiments and an innovative virtual reality (VR) system in which locust nymphs moved freely, interacting with virtual locusts, while immersed in a 3D virtual environment, Sercan Sayin and collogues show that classical models of collective behavior fail to explain the motion exhibited by swarming locusts. Sayin et al. found that locusts do not follow the fixed interaction rules assumed by traditional models, such as explicitly aligning with their moving neighbors when the density of the swarm increases. Instead, locusts behaved as if they were drawn toward other locusts – a behavior that aligns with a minimal cognitive model of spatiotemporal decision-making where an individual makes directional choices based on their own internal consensus. “Sayin et al. conclude that it is time to move beyond the conception of locusts and other organisms as moving particles behaving according to fixed spatiotemporal rules and to consider organisms as probabilistic decision-makers responding dynamically to their sensory environment,” write Camille Buhl and Stephen Simpson in a related Perspective.