Compositional generalization through abstract representations in human and artificial neural networks

TL;DR

This study demonstrates that incorporating primitive-based pretraining in artificial neural networks enhances their compositional generalization, aligning their neural and behavioral signatures more closely with humans, and supports the role of abstract representations in this process.

Contribution

It introduces a primitives pretraining method that embeds compositionality into ANNs, leading to improved generalization and neural correspondence with humans.

Findings

ANHs with primitives pretraining show better zero-shot generalization.

Primitives pretraining induces hierarchical abstract representations.

Neural signatures of compositionality in ANNs match human fMRI data.

Abstract

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.