|Title||Probing the compositionality of intuitive functions|
|Publication Type||CBMM Memos|
|Year of Publication||2016|
|Authors||Schulz, E, Tenenbaum, JB, Duvenaud, D, Speekenbrink, M, Gershman, SJ|
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.
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