Teaching and learning generalizable abstractions

A hallmark of effective teaching is that it grants learners not just a collection of facts about the world, but also a toolkit of abstractions that can be applied to solve new problems. How do humans transmit and acquire generalizable abstractions from examples? Here, we applied Bayesian models of pedagogy to a necklace-building task where teachers create necklaces to teach a learner ``motifs'' that can be flexibly recombined to create new necklaces. In Experiment 1 (N = 151), we find that human teachers produce necklaces that are simpler (i.e., have lower algorithmic complexity) than would be expected by chance, as indexed by a model that samples uniformly from all necklaces that contain the target motifs. This tendency to select simpler examples is partially captured by a pedagogical sampling model that tries to maximize the learner's belief in the underlying motifs. In Experiment 2 (N = 295), we find that simplicity is beneficial. Human learners recover the underlying motifs better when teachers produce simpler sequences, and they learn best from human teachers rather than from model-generated examples. Our results suggest that the computational principles that underlie effective communication and teaching may also provide a first step towards understanding the transmission of culturally-specific abstractions.