Using category structures to test iterated learning as a method for identifying inductive biases

Thomas L. Griffiths, Brian R. Christian, Michael L. Kalish

Research output: Contribution to journalArticlepeer-review

46 Scopus citations

Abstract

Many of the problems studied in cognitive science are inductive problems, requiring people to evaluate hypotheses in the light of data. The key to solving these problems successfully is having the right inductive biases - assumptions about the world that make it possible to choose between hypotheses that are equally consistent with the observed data. This article explores a novel experimental method for identifying the biases that guide human inductive inferences. The idea behind this method is simple: This article uses the responses produced by a participant on one trial to generate the stimuli that either they or another participant will see on the next. A formal analysis of this "iterated learning" procedure, based on the assumption that the learners are Bayesian agents, predicts that it should reveal the inductive biases of these learners, as expressed in a prior probability distribution over hypotheses. This article presents a series of experiments using stimuli based on a well-studied set of category structures, demonstrating that iterated learning can be used to reveal the inductive biases of human learners.

Original languageEnglish (US)
Pages (from-to)68-107
Number of pages40
JournalCognitive science
Volume32
Issue number1
DOIs
StatePublished - Jan 2008

All Science Journal Classification (ASJC) codes

  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience
  • Artificial Intelligence

Keywords

  • Bayesian inference
  • Induction
  • Iterated learning
  • Mathematical modeling
  • Statistics

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