Update 10th October 2024: Our systems are now restored following recent technical disruption, and we’re working hard to catch up on publishing. We apologise for the inconvenience caused. Find out more
Previous chapters have explored the capacity of neural networks for modeling cognition. This chapter looks at applications to infant cognitive development. The first section reviews the trajectory of infants' understanding of object permanence and their ability to engage in physical reasoning, and how the symbolic representation theory can interpret the phenomenon. The second section introduce examples showing that neural networks can accommodate infant reasoning development without explicit rules and symbolic representations. The third section considers the relationship between symbolic models and neural network models, exploring an argument from Fodor and Pylyshyn trying to show that artificial neural networks are not genuine competitors to symbolic accounts.
Review the options below to login to check your access.
Log in with your Cambridge Higher Education account to check access.
If you believe you should have access to this content, please contact your institutional librarian or consult our FAQ page for further information about accessing our content.