Selecting informative queries is a crucial component of learning and decision-making, where models of information searchhave been widely used to provide normative guidance. Yet a typical requirement of these models is complete informationabout the underlying probabilistic structure of the environment, which is seldom met in real-world situations. Thus,information search models are blind to the epistemic uncertainty that comes with learning through experience, and do notdistinguish between probabilities estimated from a sample of two and a sample of one million. We develop a learningparadigm where a successful strategy needs to balance the exploration of queries with high epistemic uncertainty, with theexploitation of queries already known to be useful. We show that a Bayesian sampling variant of traditional informationsearch models learns faster and performs better, but most surprisingly, that a simple take-the-difference heuristic (TTD)performs competitively using only the absolute difference between observed frequencies.

A key question individuals face in any social learning environment is when to innovate alone and when to imitate others. Previous simulation results have found that the best performing groups exhibit an intermediate balance, yet it is still largely unknown how individuals collectively negotiate this balance. We use an immersive collective foraging experiment, implemented in the Minecraft game engine, facilitating unprecedented access to spatial trajectories and visual field data. The virtual environment imposes a limited field of view, creating a natural trade-off between allocating visual attention towards individual search or to look towards peers for social imitation. By analyzing foraging patterns, social interactions (visual and spatial), and social influence, we shine new light on how groups collectively adapt to the fluctuating demands of the environment through specialization and selective imitation, rather than homogeneity and indiscriminate copying of others.