title: Probabilistic inference in human infants.
abstract: In this chapter, we review empirical evidence in support of infants' ability to make rudimentary probabilistic inferences. A recent surge of research in cognitive developmental psychology examines whether human learners, from infancy through adulthood, reason in ways consistent with Bayesian inference. However, when exploring this question an important first step is to identify the available inference mechanisms and computational machinery that might allow infants and young children to make inductive inferences. A number of recent studies have asked if infants may be "intuitive statisticians," making inferences about the relationship between samples and populations in both looking-time and choice tasks. Furthermore, infants make these inferences under a variety of sampling conditions and integrate prior domain knowledge into their probability calculations. The competences demonstrated in the reviewed experiments appear to draw on an intuitive probability notion that is early emerging and does not appear to be available for conscious reflection.
authors.affiliation: Department of Psychology, University of California, Berkeley, Berkeley, CA 94720, USA. smdeniso@berkeley.edu
authors.forename: Stephanie
authors.initials: S
authors.lastname: Denison
authors.forename: Fei
authors.initials: F
authors.lastname: Xu
mesh_terms: D001288:Attention; D001499:Bayes Theorem; D002657:Child Development; D003071:Cognition; D003118:Color Perception; D006801:Humans; D007223:Infant; D009042:Motivation; D009949:Orientation; D010364:Pattern Recognition, Visual; D011337:Probability Learning; D011340:Problem Solving; D002666:Psychology, Child