Commentary: new kids on the connectionist modeling block

2000

Although the reasons for the success of computer simulations of psychology are often difficult to identify, it is possible to make some progress through systematic experimentation. Reasons for the success of cascadecorrelation models of cognitive development are identified in two case studies. Cascade-correlation is a generative neural network model that constructs its own topology as it recruits hidden units. Capturing correct stage sequences in the integration of velocity, time, and distance cues requires a system that grows in computational power while it learns. Static networks are either too weak or too powerful to capture the full range of stages. Simulating the variation and stages in acquisition of the semantics of English personal pronouns requires sensitivity to differing amounts of addressed and non-addressed speech. Just as with children, networks benefit from the opportunity to hear personal pronouns used in exchanges between o ther speakers. Other simulations suggest t...

Cognitive Develop, 1996

This article presents a novel computational framework for modeling cognitive development. The new modeling paradigm provides a language with which to compare and contrast radically different facets of children's knowledge. Concepts from the study of machine learning are used to explore the power of connectionist networks that construct their own architectures during learning. These so-called generative algorithms are shown to escape from Fodor's (1980) critique of constructivist development. We describe one generative connectionist algorithm (cascade-correlation) in detail. We report on the successful use of the algorithm to model cognitive development on balance scale phenomena; seriation; the integration of velocity, time, and distance cues; prediction of effect sizes from magnitudes of causal potencies and effect resistances; and the acquisition of English personal pronouns. The article demonstrates that computer models are invaluable for illuminating otherwise obscure discussions.

Connectionist and dynamical systems approaches explain human thought, language and behavior in terms of the emergent consequences of a large number of simple noncognitive processes. We view the entities that serve as the basis for structured probabilistic approaches as sometimes useful but often misleading abstractions that have no real basis in the actual processes that give rise to linguistic and cognitive abilities or the development of these abilities. While structured probabilistic approaches can be useful in determining what would be optimal under certain assumptions, we suggest that approaches such as the connectionist and dynamical systems approaches, which focus on explaining the mechanisms giving rise to cognition, will be essential in achieving a full understanding of cognition and development. 2 3 4 5 6 7 8

Journal of theoretical Biology, 1991

Encyclopedia of the Sciences of Learning, 2012

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認知科学, 2003

Her research investigates memory development and the nature of representations leading to behavioral dissociations, through neural network modeling and behavioral testing of infants and children. She serves on a grant review panel of the National Institute of Health, and is a recipient of the Boyd McCandless Award for early contributions to developmental Psychology. O'Reilly received an A.B. in Psychology from Harvard University in 1989, and an M.S. and Ph.D. in Psychology from Carnegie Mellon University in 1992 and 1996. His research focuses on the specialization of function in and interactions between the hippocampus, prefrontal cortex, and posterior neocortex in learning, memory, and controlled processing. He is an Associate Editor of "Cognitive Science" and a Reviewing Editor for "Hippocampus," and is a recipient of an Excellence in Teaching award.

I discuss a connectionist model, based on Elman's (1990, 1991) Simple Recurrent Network, of the acquisition of complex syntactic structure. While not intended as a detailed model of the process children go through in acquiring natural languages, the model helps clarify concepts that may be useful for understanding the development of complex abilities. It provides evidence that connectionist learning can produce stage-wise development emergently. It is consistent with prior work on connectionist models emphasizing their capability of computing in ways that are not possible within the symbolic paradigm (Siegelmann, 1999). On the other hand, it suggests that one mechanism of the symbolic paradigm (a pushdown automaton) may be identified with an attractor of the learning process. Thus, the model provides a concrete example of what might be called "emergence of new conceptual structure during development" and suggests that we need to use both dynamical systems theory and symbolic computation theory to make sense of it.

English linguistics, 1999

Toward a Unified Theory of Development Connectionism and Dynamic System Theory Re-Consider, 2009

This chapter discusses old-fashioned cognitive development from the point of view of two modern approaches, connectionism and nonlinear dynamical systems theory. The main assertion in both connectionist and dynamic systems approaches is that higher cognitive functioning is largely based on nonsymbolic, graded, and dynamic properties, of which these same approaches provide the best account. The chapter argues that the claim concerning nonsymbolic higher order cognition is overstated and explains its position by focusing on sudden transitions in cognitive development.

1994