We propose six Information-Processing Principles (IPPs) that together describe a constructive, hierarchical system by which infants come to understand objects and events in the world around them. We then demonstrate the applicability of these principles to four specific domains of infant perception and/or cognition, (i.e., form perception, object unity, complex pattern perception, and understanding of causal events). In each case empirical developmental changes appear to be consistent with the IPPs. We then present the Constructivist Learning Architecture, a computational model of infant cognitive development. This model is based on the IPPs, and uses self-organizing, neurally based techniques from Kohonen (1997) and Hebb (1949). We then apply the model to the complex domain of infant understanding of causal events, and replicate many of the developmental changes found empirically. Finally, we discuss the applicability of this constructivist approach to infant cognitive development in general.

Key words: information-processing principles, constructivist learning architecture, infant cognition.

Key words: cognition, mathematics

Key words: radical constructivism, Jean Piaget

Commentary on “Stepping off the pendulum: Why only an action-based approach can transcend the nativist–empiricist debate” by J. Allen and M. Bickhard.

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.

Artificial Life is the study of all phenomena of the living world through their repro¬duction in artificial systems. We argue that Artificial Life models of evolution and devel¬opment offer a new set of theoretical and methodological tools for investigating Piaget’s ideas. The concept of an Artificial Life Neural Network (ALNN) is first introduced, and contrasted with the study of other recent approaches to modeling development. We then illustrate how several key elements of Piaget’s theory of cognitive development (e.g., sensorimotor schemata, perception-action integration) can be investigated within the Ar¬tificial Life framework. We conclude by discussing possible new directions of Artificial Life research that will help to elaborate and extend Piaget’s developmental framework.

Key words: Artificial life, Piaget, evolution

Key words: science

We examine the contributions of dynamic systems theory to the field of cognitive development, focusing on modeling using dynamic neural fields. After introducing central concepts of dynamic field theory (DFT), we probe empirical predictions and findings around two examples – the DFT of infant perseverative reaching that explains Piaget’s A-not-B error and the DFT of spatial memory that explain changes in spatial cognition in early development. Review of the literature around these examples reveals that computational modeling is having an impact on empirical research in cognitive development; however, this impact does not extend to neural and clinical research. Moreover, there is a tendency for researchers to interpret models narrowly, anchoring them to specific tasks. We conclude on an optimistic note, encouraging both theoreticians and experimentalists to work toward a more theory-driven future.

Key words: dynamic systems theory, spatial memory, perseveration, neural networks, object concept.