@article {2109,
	title = {Probing the compositionality of intuitive functions},
	year = {2016},
	month = {05/2016},
	abstract = {<p>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.</p>
},
	author = {Eric Schulz and Joshua B. Tenenbaum and David Duvenaud and Maarten Speekenbrink and Samuel J Gershman}
}
