Learning to perform auditory discriminations from observation is efficient but less robust than learning from experience
Social learning enables complex societies. However, it is largely unknown how insights obtained from observation compare with insights gained from trial-and-error, in particular in terms of their robustness. We use aversive reinforcement to train \"experimenter\" zebra finches to discriminate between auditory stimuli in the presence of an \"observer\" finch. We find that experimenters are slow to successfully discriminate the stimuli but immediately generalize their ability to a new set of similar stimuli. By contrast, observers subjected to the same task instantly discriminate the initial stimulus set, but require more time for successful generalization. Drawing upon machine learning insights, we suggest that observer learning has evolved to rapidly absorb sensory statistics without pressure to minimize neural resources, whereas learning from experience is endowed with a form of regularization that enables robust inference.