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Abstract

We present a novel experimental method for identifying the inductive biases of human learners. The key idea behind this method is simple: we use participants’ re- sponses on one trial to generate the stimuli they see on the next. A theoretical analysis of this “iterated learn- ing” procedure, based on the assumption that learners are Bayesian agents, predicts that it should reveal the inductive biases of the learners, as expressed in a prior probability distribution. We test this prediction through two experiments in iterated category learning.

@inproceedings{Griffiths06iteratedLearning,
  author={Thomas L. Griffiths and Brian R. Christian and Michael L. Kalish},
  title={Revealing priors on category structures through iterated learning},
  year={2006},
  booktitle={Proceedings of the 28th Annual Conference of the Cognitive Science Society},
  url={http://www.isrl.uiuc.edu/~amag/langev/paper/Griffiths06iteratedLearning.html}
}

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