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A Relational Inductive Bias for Dimensional Abstraction in Neural Networks
The human cognitive system exhibits remarkable flexibility and generalization capabilities, partly due to its ability to form low-dimensional, compositional representations of the environment.
Comparing Abstraction in Humans and Large Language Models Using Multimodal Serial Reproduction
To investigate the effect language on the formation of abstractions, we implement a novel multimodal serial reproduction framework by asking people who receive a visual stimulus to reproduce it in a linguistic format, and vice versa.
Human-Like Geometric Abstraction in Large Pre-trained Neural Networks
Humans possess a remarkable capacity to recognize and manipulate abstract structure, which is especially apparent in the domain of geometry.
Relational Constraints On Neural Networks Reproduce Human Biases towards Abstract Geometric Regularity
Uniquely among primates, humans possess a remarkable capacity to recognize and manipulate abstract structure in the service of task goals across a broad range of behaviors.
The Relational Bottleneck as an Inductive Bias for Efficient Abstraction
A central challenge for cognitive science is to explain how abstract concepts are acquired from limited experience.
