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Using Category Structures to Test Iterated Learning as a Method for Identifying Inductive Biases
Author(s) -
Griffiths Thomas L.,
Christian Brian R.,
Kalish Michael L.
Publication year - 2008
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1080/03640210701801974
Subject(s) - inductive bias , iterated function , computer science , inductive reasoning , set (abstract data type) , bayesian probability , machine learning , artificial intelligence , test (biology) , key (lock) , cognitive psychology , multi task learning , psychology , mathematics , task (project management) , mathematical analysis , paleontology , management , computer security , programming language , economics , biology
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.