Superspace extrapolation reveals inductive biases in function learning

We introduce a new approach for exploring how humans learn and represent functional relationships based on limited observations. We focus on a problem called superspace extrapolation , where learners observe training examples drawn from an n -dimensional space and must extrapolate to an n + 1 - dimensional superspace of the training examples. Many existing psychological models predict that superspace extrapolation should be fundamentally under-determined, but we show that humans are able to extrapolate both linear and non-linear functions under these conditions. We also show that a Bayesian model can account for our results given a hypothesis space that includes families of simple functional relationships