Relational Inductive Biases Enable Efficient Learning of Abstract Concepts in Neural Networks

Core Concepts

The relational bottleneck is an architectural inductive bias that enables neural networks to learn abstract relational concepts in a data-efficient manner, by constraining information processing to focus on relations between objects rather than the attributes of individual objects.

Abstract

The content discusses the relational bottleneck as a novel approach to reconciling symbolic and connectionist approaches to modeling the acquisition of abstract concepts. The key idea is that by restricting information processing to focus only on relations between objects, rather than the attributes of individual objects, neural networks can learn abstract symbol-like representations in a data-efficient manner. The paper first provides a general characterization of the relational bottleneck principle, drawing on information theory. It then reviews three recently proposed neural network architectures that implement this principle in different ways: The Emergent Symbol Binding Network (ESBN) separates the processing of perceptual information (fillers) from the learning of abstract representations (roles), enabling rapid learning and systematic generalization of relational patterns. The Compositional Relation Network (CoRelNet) computes a relation matrix over all pairs of object embeddings, ensuring that downstream processing depends only on this relational information. The Abstractor architecture uses a novel "relational cross-attention" mechanism to isolate relational information from perceptual content. The paper then discusses how the relational bottleneck principle can help explain phenomena in human cognition, such as the developmental trajectory of learning to count, and capacity limits in cognition. It also considers how this principle may be implemented in the human brain. Overall, the relational bottleneck provides a unifying principle for understanding how neural networks can learn abstract concepts in a data-efficient manner, bridging the gap between symbolic and connectionist approaches to modeling human cognition.

Stats

"Human learners acquire abstract concepts from limited experience." "Understanding how the mind and brain accomplish this has been a central challenge of cognitive science." "A long tradition in cognitive science and AI holds that this capacity for abstraction depends on processes akin to symbolic programs." "An alternative approach, connectionism, has for decades explored how cognitive abstractions might emerge through experience in general-purpose neural architectures." "Large language models can solve various analogy problems at a level equal to that of college students, but depend on exposure to a much larger training corpus than individual humans receive in an entire lifetime."

Quotes

"A central challenge for cognitive science is to explain how abstract concepts are acquired from limited experience." "Human cognition displays a remarkable ability to transcend the specifics of limited experience to entertain highly general, abstract ideas." "The relational bottleneck principle suggests a novel way to bridge the gap. By restricting information processing to focus only on relations, the approach encourages abstract symbol-like mechanisms to emerge in neural networks."

Key Insights Distilled From

The Relational Bottleneck as an Inductive Bias for Efficient Abstraction

by Taylor W. We... at arxiv.org 04-01-2024

https://arxiv.org/pdf/2309.06629.pdf

Deeper Inquiries

How might the relational bottleneck principle be extended to capture 'content effects', in which abstract reasoning is influenced by the specific content under consideration?

To extend the relational bottleneck principle to capture 'content effects', we can introduce a more graded version of the bottleneck. This graded version would allow for a controlled amount of non-relational information to pass through the bottleneck, alongside the relational information. By incorporating this flexibility, the model can account for how abstract reasoning processes are influenced by the specific content under consideration. This approach would enable the model to adapt to different contexts and incorporate content-specific nuances while still maintaining a focus on relational abstraction.

Can the relational bottleneck principle be applied to symbolic (or neuro-symbolic) models, in addition to neural networks?

Yes, the relational bottleneck principle can be applied to symbolic or neuro-symbolic models in addition to neural networks. For symbolic models, the relational bottleneck can be implemented as a constraint that forces perceptual inputs to be recoded in terms of relations. This constraint would guide the learning process towards focusing on relational patterns rather than individual attributes, aligning with the core idea of the relational bottleneck. In neuro-symbolic models, the relational bottleneck can serve as an architectural bias that promotes the development of genuinely relational representations within the neural components of the model.

What is the relationship between the relational bottleneck and traditional cognitive models of analogical reasoning?

The relational bottleneck and traditional cognitive models of analogical reasoning share a fundamental focus on the importance of relations in cognitive processes. Analogical reasoning involves mapping similarities and relationships between different entities or concepts, which aligns closely with the relational processing encouraged by the relational bottleneck. Both frameworks emphasize the role of relational structures in abstract thinking and generalization across different domains. The relational bottleneck can be seen as a computational instantiation of the principles underlying analogical reasoning, providing a mechanism for inducing abstract concepts efficiently through a focus on relational patterns. By promoting the emergence of relational representations in neural networks, the relational bottleneck bridges the gap between connectionist and symbolic approaches, offering a novel perspective on how abstract concepts are acquired from limited experience.