  Should we care whether AI systems have representations of the world that are
similar to those of humans? We provide an information-theoretic analysis that
suggests that there should be a U-shaped relationship between the degree of
representational alignment with humans and performance on few-shot learning
tasks. We confirm this prediction empirically, finding such a relationship in
an analysis of the performance of 491 computer vision models. We also show that
highly-aligned models are more robust to both natural adversarial attacks and
domain shifts. Our results suggest that human-alignment is often a sufficient,
but not necessary, condition for models to make effective use of limited data,
be robust, and generalize well.
