  Living in groups brings benefits to many animals, such as a protection
against predators and an improved capacity for sensing and making decisions
while searching for resources in uncertain environments. A body of studies has
shown how collective behaviors within animal groups on the move can be useful
for pooling information about the current state of the environment. The effects
of interactions on collective motion have been mostly studied in models of
agents with no memory. Thus, whether coordinated behaviors can emerge from
individuals with memory and different foraging experiences is still poorly
understood. By means of an agent based model, we quantify how individual memory
and information fluxes can contribute to improving the foraging success of a
group in complex environments. In this context, we define collective learning
as a coordinated change of behavior within a group resulting from individual
experiences and information transfer. We show that an initially scattered
population of foragers visiting dispersed resources can gradually achieve
cohesion and become selectively localized in space around the most salient
resource sites. Coordination is lost when memory or information transfer among
individuals is suppressed. The present modelling framework provides predictions
for empirical studies of collective learning and could also find applications
in swarm robotics and motivate new search algorithms based on reinforcement.
