title: Hierarchical Bayesian models of cognitive development.
abstract: This article provides an introductory overview of the state of research on Hierarchical Bayesian Modeling in cognitive development. First, a brief historical summary and a definition of hierarchies in Bayesian modeling are given. Subsequently, some model structures are described based on four examples in the literature. These are models for the development of the shape bias, for learning ontological kinds and causal schemata as well as for the categorization of objects. The Bayesian modeling approach is then compared with the connectionist and nativist modeling paradigms and considered in view of Marr's (1982) three description levels of information-processing mechanisms. In this context, psychologically plausible algorithms and ideas of their neural implementation are presented. In addition to criticism and limitations of the approach, research needs are identified.
authors.affiliation: Faculty of Aerospace Engineering, Human Factors Institute, Universität der Bundeswehr München, Werner-Heisenberg-Weg 39, 85577, Neubiberg, Germany. thomas.glassen@unibw.de.
authors.forename: Thomas
authors.initials: T
authors.lastname: Glassen
authors.affiliation: Faculty of Aerospace Engineering, Human Factors Institute, Universität der Bundeswehr München, Werner-Heisenberg-Weg 39, 85577, Neubiberg, Germany.
authors.forename: Verena
authors.initials: V
authors.lastname: Nitsch
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003071:Cognition / Q000502:physiology*; D003491:Cybernetics; D006801:Humans; D007858:Learning
keywords: Abstraction; Acquisition; Bayesian; Hierarchical; Knowledge; Modeling