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• W2400084313 abstract "How do people learn about complex functional structure? Taking inspiration from other areas of cognitive science, we propose that this is accomplished 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. We show that participants prefer compositional over non-compositional function extrapolations, that samples from the human prior over functions are best described by a compositional model, and that people perceive compositional functions as more predictable than their non-compositional but otherwise similar counterparts. We argue that the compositional nature of intuitive functions is consistent with broad principles of human cognition." @default.
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• W2400084313 date "2016-05-26" @default.
• W2400084313 modified "2023-09-24" @default.
• W2400084313 title "Probing the Compositionality of Intuitive Functions" @default.
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