title: Probabilistic models, learning algorithms, and response variability: sampling in cognitive development.
abstract: Although probabilistic models of cognitive development have become increasingly prevalent, one challenge is to account for how children might cope with a potentially vast number of possible hypotheses. We propose that children might address this problem by 'sampling' hypotheses from a probability distribution. We discuss empirical results demonstrating signatures of sampling, which offer an explanation for the variability of children's responses. The sampling hypothesis provides an algorithmic account of how children might address computationally intractable problems and suggests a way to make sense of their 'noisy' behavior.
authors.affiliation: Department of Psychology, Rutgers University at Newark, Newark, NJ 07102 USA. Electronic address: elizabeth.bonawitz@rutgers.edu.
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Bonawitz
authors.affiliation: University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada.
authors.forename: Stephanie
authors.initials: S
authors.lastname: Denison
authors.affiliation: University of California at Berkeley, Berkeley, CA, USA.
authors.forename: Thomas L
authors.initials: TL
authors.lastname: Griffiths
authors.affiliation: University of California at Berkeley, Berkeley, CA, USA.
authors.forename: Alison
authors.initials: A
authors.lastname: Gopnik
mesh_terms: D000293:Adolescent; D000465:Algorithms; D002648:Child; D002657:Child Development; D003071:Cognition / Q000502:physiology*; D006801:Humans; D007858:Learning / Q000502:physiology*; D011336:Probability
keywords: approximate Bayesian inference; causal learning; cognitive development; sampling hypothesis