title: Children can solve Bayesian problems: the role of representation in mental computation.
abstract: Can children reason the Bayesian way? We argue that the answer to this question depends on how numbers are represented, because a representation can do part of the computation. We test, for the first time, whether Bayesian reasoning can be elicited in children by means of natural frequencies. We show that when information was presented to fourth, fifth, and sixth graders in terms of probabilities, their ability to estimate the Bayesian posterior probability was zero. Yet when the same information was presented in natural frequencies, Bayesian reasoning showed a steady increase from fourth to sixth grade, reaching an average level of 19, 39, and 53%, respectively, in two studies. Sixth graders' performance with natural frequencies matched the performance of adults with probabilities. But this general increase was accompanied by striking individual differences. More than half of the sixth graders solved most or all problems, whereas one third could not solve a single one. An analysis of the children's responses provides evidence for the use of three non-Bayesian strategies. These follow an overlapping wave model of development and continue to be observed in the minds of adults. More so than adults' probabilistic reasoning, children's reasoning depends on a proper representation of information.
authors.affiliation: Key Lab of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
authors.forename: Liqi
authors.initials: L
authors.lastname: Zhu
authors.forename: Gerd
authors.initials: G
authors.lastname: Gigerenzer
mesh_terms: D000328:Adult; D001499:Bayes Theorem; D002648:Child; D002657:Child Development; D003071:Cognition; D006801:Humans; D008433:Mathematics