  Humans have the ability to rapidly understand rich combinatorial concepts
from limited data. Here we investigate this ability in the context of auditory
signals, which have been evolved in a cultural transmission experiment to study
the emergence of combinatorial structure in language. We propose a
neuro-symbolic generative model which combines the strengths of previous
approaches to concept learning. Our model performs fast inference drawing on
neural network methods, while still retaining the interpretability and
generalization from limited data seen in structured generative approaches. This
model outperforms a purely neural network-based approach on classification as
evaluated against both ground truth and human experimental classification
preferences, and produces superior reproductions of observed signals as well.
Our results demonstrate the power of flexible combined neural-symbolic
architectures for human-like generalization in raw perceptual domains and
offers a step towards developing precise computational models of inductive
biases in language evolution.
