Computational Developmental Psychology

Overview

Despite decades of scientific research, the core issues of child development remain too complex to be explained by traditional verbal theories. These issues include structure and transition, representation and processing, innate and experiential determinants of development, stages of development, the purpose and end of development, and the relation between knowledge and learning. In this book Thomas Shultz shows how computational modeling can be used to capture these complex phenomena, and in so doing he lays the foundation for a new subfield of developmental psychology, computational developmental psychology.

A principal approach in developmental thinking is the constructivist one. Constructivism is the Piagetian view that the child builds new cognitive structures by using current mental structures to understand new events. In this book Shultz features constructivist models employing networks that grow as well as learn. This allows models to implement synaptogenesis and neurogenesis in a way that allows qualitative changes in processing mechanisms. The book's appendices provide additional background on the mathematical concepts used, and a companion Web site contains easy-to-use computational packages.

Table of Contents

  1. Preface
  2. Acknowledgements
  3. Introduction
  4. A Neural-Network Primer
  5. Knowledge Representation
  6. Developmental Transitions
  7. Stages of Development
  8. Objections and Rebuttals
  9. On the Horizon
  10. Understanding Derivatives
  11. Derivative of Error with Respect to Output
  12. Derivative of the Asigmoid Activation Function
  13. Weight Adjustments in Quickprop
  14. Notes
  15. References
  16. Index