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Compositional generalization through abstract representations in human and artificial neural networks
Takuya Ito · Tim Klinger · Doug Schultz · John Murray · Michael Cole · Mattia Rigotti

Wed Nov 30 02:00 PM -- 04:00 PM (PST) @ Hall J #738

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 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 humans 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.

Author Information

Takuya Ito (Yale University)

Broadly, my interest and experience is at the interface of neuroscience and machine learning. I study information processing in brains and neural models. More recently, my specific interests lie in understanding the structure of multi-task and compositional neural representations, and how they emerge in natural and artificial settings. My motivation is twofold: 1) To understand properties of the brain to better inform the design of computational models (e.g., to achieve and excede human-like performance); 2) To better understand cognitive/computational deficits in psychiatric illnesses.

Tim Klinger (IBM Research AI)
Doug Schultz (University of Nebraska, Lincoln)
John Murray (Yale University)
Michael Cole (Rutgers University - Newark)
Mattia Rigotti (IBM Research AI)

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