The Bayesian revolution approaches psychological development.
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.
Shultz
Thomas R
TR
Department of Psychology, McGill University, Montreal, Quebec, Canada. Thomas.shultz@mcgill.ca
D001499:Bayes Theorem; D002648:Child; D002657:Child Development / Q000502:physiology*; D002675:Child, Preschool; D003071:Cognition / Q000502:physiology*; D006801:Humans; D007223:Infant; D008960:Models, Psychological; D014706:Verbal Learning / Q000502:physiology*