  Humans have a remarkable ability to rapidly generalize to new tasks that is
difficult to reproduce in artificial learning systems. Compositionality has
been proposed as a key mechanism supporting generalization in humans, but
evidence of its neural implementation and impact on behavior is still scarce.
Here we study the computational properties associated with compositional
generalization in both humans and artificial neural networks (ANNs) on a highly
compositional task. First, we identified behavioral signatures of compositional
generalization in humans, along with their neural correlates using whole-cortex
functional magnetic resonance imaging (fMRI) data. Next, we designed
pretraining paradigms aided by a procedure we term {\em primitives pretraining}
to endow compositional task elements into ANNs. We found that ANNs with this
prior knowledge had greater correspondence with human behavior and neural
compositional signatures. Importantly, primitives pretraining induced abstract
internal representations, excellent zero-shot generalization, and
sample-efficient learning. Moreover, it gave rise to a hierarchy of abstract
representations that matched human fMRI data, where sensory rule abstractions
emerged in early sensory areas, and motor rule abstractions emerged in later
motor areas. Our findings give empirical support to the role of compositional
generalization in human behavior, implicate abstract representations as its
neural implementation, and illustrate that these representations can be
embedded into ANNs by designing simple and efficient pretraining procedures.
