Compositional inductive biases in function learning
Abstract How do people recognize and learn about complex functional structure? Taking inspiration from other areas of cognitive science, we propose that this is achieved by harnessing compositionality: complex structure is decomposed into simpler building blocks. We formalize this idea within the framework of Bayesian regression using a grammar over Gaussian process kernels, and compare this approach with other structure learning approaches. Participants consistently chose compositional (over non-compositional) extrapolations and interpolations of functions. Experiments designed to elicit priors over functional patterns revealed an inductive bias for compositional structure. Compositional functions were perceived as subjectively more predictable than non-compositional functions, and exhibited other signatures of predictability, such as enhanced memorability and reduced numerosity. Taken together, these results support the view that the human intuitive theory of functions is inherently compositional. Highlights This paper investigates how people recognize and learn about complex functional structure. We formalize this idea within the framework of Bayesian regression using a grammar over Gaussian process kernels. Our model provides a good account of participants’ completion, learning, perception, and memorization of functional structure paradigms Our results support the view that the human intuitive theory of functions is inherently compositional.
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