Shultz, T. R. (2007). The Bayesian revolution approaches psychological development. Developmental Science, 10, 357-364.



This commentary reviews five articles that apply Bayesian ideas to psychological development, some with psychology experiments, some with computational modeling, and some with both experiments and modeling. The reviewed work extends the current Bayesian revolution into tasks often studied in children, such as causal learning and word learning, and provides evidence that children’s performance can be optimal in a Bayesian sense. There remains much to be done in terms of understanding how representations are created, how development occurs, how Bayesian computation might be neurally implemented, and in reconciling the new work with older evidence that even skilled adults are incompetent Bayesians.


Copyright notice

Abstracts, papers, chapters, and other documents are posted on this site as an efficient way to distribute reprints. The respective authors and publishers of these works retain all of the copyrights to this material. Anyone copying, downloading, bookmarking, or printing any of these materials agrees to comply with all of the copyright terms. Other than having an electronic or printed copy for fair personal use, none of these works may be reposted, reprinted, or redistributed without the explicit permission of the relevant copyright holders.


To obtain a PDF reprint of this particular article, signal your agreement with these copyright terms by clicking on the statement below.


I agree with all of these copyright terms PDF 50KB