Connectionist models of development, developmental disorders and individual differences

Introduction The computational modelling of cognitive processes offers several advantages. One of the most notable is theory clarification. Verbally specified theories permit the use of vague, ill-defined terms that may mask errors of logic or consistency, errors that often become apparent when formal implementation forces these terms to be clarified. Whereas in the domain of intelligence research, one may refer to a more clever cognitive system as being 'faster', an implemented model of that system must specify what 'speed' really means. Whereas in the domain of developmental research, one may refer to a more developed cognitive system as containing 'more complexity', an implemented model must specify what 'complexity' really means. Whereas in the domain of atypical development, one may refer to a disordered cognitive system as having 'insufficient processing resources', an implemented model must specify what a 'processing resource' really means. Computer models have recently been applied to each of these domains – individual differences, cognitive development, and atypical development – against a background of pre-existing verbal theories speculating on what cognitive mechanisms might underlie variations in each domain. The aim of this chapter is to examine how computational implementation has forced conceptual clarification of these mechanisms, and in particular, how implementation has shed light on the theoretical relation between the domains. Our discussion focuses on one particular class of widely used model, connectionist networks. The crux of the issue is as follows. The domains of individual differences, cognitive development, and atypical development each represent a form of cognitive variability: they deal in terms of superior or inferior performance on cognitive tasks. Each computational model contains parameters that alter the system's performance on the task it is built to address. Therefore, such computational parameters stand as possible mechanistic explanations for variability in performance. Implemented models of individual differences, of cognitive Connectionist models 2 development, and of atypical development have appealed to certain computational parameters to explain superior or inferior performance on cognitive tasks. We can ask firstly, do these models appeal to the same parameters in each case, or different ones? And secondly, what computational role do the parameters play in each model? These two questions can be recast in theoretical terms: do individual differences, cognitive development, and atypical development lie on the same dimension or on different dimensions? And what are the precise computational mechanisms that underlie the dimensions? Our chapter addresses these questions. In the following sections, we …