@article {3440,
	title = {Compositional inductive biases in function learning.},
	journal = {Cogn Psychol},
	volume = {99},
	year = {2017},
	month = {2017 Dec},
	pages = {44-79},
	abstract = {<p>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.</p>
},
	issn = {1095-5623},
	doi = {10.1016/j.cogpsych.2017.11.002},
	url = {https://www.sciencedirect.com/science/article/pii/S0010028517301743?via\%3Dihub},
	author = {Eric Schulz and Joshua B. Tenenbaum and David Duvenaud and Maarten Speekenbrink and Samuel J Gershman}
}
@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}
}
