LAUSR

Statistical learning is a key mechanism for detecting regularities in sensory inputs1,2. Among its functions is the ability to extract regularities from sequences (of sounds, objects, odors, etc.)3, enabling species to predict future events and guide behavior. This capacity has been demonstrated in vertebrates2, including human infants4, non-human primates5, and birds6. However, the minimum computational architecture required for statistical learning remains unclear. To address this issue, we studied statistical learning in the honey bee (Apis mellifera), an invertebrate model for learning studies7. We show that bees learn and recall the temporal structure of sequences of odorants, suggesting that statistical learning is a fundamental component of a conserved cognitive toolkit present even in invertebrates.