Payoff selectivity drives collective intelligence during dynamic resource tracking in humans

Abstract

Humans’ abilities for complex social learning and collective adaptation stand out across species. Yet it is largely unknown how people integrate dynamically changing personal and social cues in realistic environments, resulting in collective intelligence or maladaptive behavior. Here, we report an online 3D immersive-reality experiment in which participants (n = 621) search for and track a mobile resource under realistic visual–spatial constraints. Participants complete the task either alone or in groups of five with different types of social information and resource speeds. Contrasting earlier work, participants in groups outperform solitary individuals, but only when payoff information was available. High-resolution visual field data and movement trajectories reveal that payoff visibility prompts adaptive recalibration of visual information flow, thereby enabling flexible adaptation. Computational models of fine-grained movement decisions and agent-based simulations show that payoff information lets participants dynamically identify and selectively respond to reliable social cues, thus unlocking the group’s collective potential. Humans rely on complex social learning and collective adaptation to navigate dynamically changing environments. The authors show that human collective intelligence in a dynamic, naturalistic task depends on access to payoff information.