title: Connectionist modeling of developmental changes in infancy: approaches, challenges, and contributions.
abstract: Connectionist models have been applied to many phenomena in infant development including perseveration, language learning, categorization, and causal perception. In this article, we discuss the benefits of connectionist networks for the advancement of theories of early development. In particular, connectionist models contribute novel testable predictions, instantiate the theorized mechanism of change, and create a unifying framework for understanding infant learning and development. We relate these benefits to the 2 primary approaches used in connectionist models of infant development. The first approach employs changes in neural processing as the basis for developmental changes, and the second employs changes in infants' experiences. The review sheds light on the unique hurdles faced by each approach as well as the challenges and solutions related to both, particularly with respect to the identification of critical model components, parameter specification, availability of empirical data, and model comparison. Finally, we discuss the future of modeling work as it relates to the study of development. We propose that connectionist networks stand to make a powerful contribution to the generation and revision of theories of early child development. Furthermore, insights from connectionist models of early development can improve the understanding of developmental changes throughout the life span.
authors.affiliation: Department of Psychology, Carnegie Mellon University.
authors.forename: Yevdokiya
authors.initials: Y
authors.lastname: Yermolayeva
authors.affiliation: Department of Psychology, Carnegie Mellon University.
authors.forename: David H
authors.initials: DH
authors.lastname: Rakison
mesh_terms: D002657:Child Development / Q000502:physiology*; D006801:Humans; D007223:Infant; D016571:Neural Networks, Computer