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23 - Computational Models of Developmental Psychology

from Part IV - Computational Modeling in Various Cognitive Fields

Published online by Cambridge University Press:  21 April 2023

By
Thomas R. Shultz  and
Ardavan S. Nobandegani
Edited by
Ron Sun
Ron Sun
Affiliation:
Rensselaer Polytechnic Institute, New York
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Summary

This chapter reviews contemporary computational models of psychological development in a historical context, including those based on symbolic rules, artificial neural networks, dynamic systems, robotics, and Bayesian ideas. Emphasis is placed on newer work and the insights that simulation can provide into developmental mechanisms. Within space limitations, coverage is both sufficiently broad to provide a general overview of the field and sufficiently detailed to facilitate understanding of important techniques. Prospects for integrating the dominant approaches of neural networks and Bayesian methods are explored. There is also speculation about how deep-learning networks might begin to impact developmental modeling by increasing the realism of training patterns, particularly in visual perception.

Keywords

psychological developmentcomputer simulationsymbolic rulesartificial neural networksdynamic systemsroboticsBayesian ideas
Type
Chapter
Information
The Cambridge Handbook of Computational Cognitive Sciences , pp. 769 - 794
Publisher: Cambridge University Press
Print publication year: 2023

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