title: Revealing human inductive biases for category learning by simulating cultural transmission.
abstract: We explored people's inductive biases in category learning--that is, the factors that make learning category structures easy or hard--using iterated learning. This method uses the responses of one participant to train the next, simulating cultural transmission and converging on category structures that people find easy to learn. We applied this method to four different stimulus sets, varying in the identifiability of their underlying dimensions. The results of iterated learning provide an unusually clear picture of people's inductive biases. The category structures that emerge often correspond to a linear boundary on a single dimension, when such a dimension can be identified. However, other kinds of category structures also appear, depending on the nature of the stimuli. The results from this single experiment are consistent with previous empirical findings that were gleaned from decades of research into human category learning.
authors.affiliation: Computer Science Division, University of California, Berkeley, CA, USA.
authors.forename: Kevin R
authors.initials: KR
authors.lastname: Canini
authors.forename: Thomas L
authors.initials: TL
authors.lastname: Griffiths
authors.forename: Wolf
authors.initials: W
authors.lastname: Vanpaemel
authors.forename: Michael L
authors.initials: ML
authors.lastname: Kalish
mesh_terms: D000328:Adult; D003210:Concept Formation / Q000502:physiology*; D006801:Humans; D007858:Learning / Q000502:physiology*; D010364:Pattern Recognition, Visual / Q000502:physiology*; D055815:Young Adult