Skip to yearly menu bar Skip to main content


Probing the Compositionality of Intuitive Functions

Eric Schulz · Josh Tenenbaum · David Duvenaud · Maarten Speekenbrink · Samuel J Gershman

Area 5+6+7+8 #164

Keywords: [ (Cognitive/Neuroscience) Perception ] [ Gaussian Processes ] [ (Other) Cognitive Science ]


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.

Live content is unavailable. Log in and register to view live content