The developmental process can provide important insights into various cognitive phenomena, often by making cognitive failures particularly stark. A great example of this is the A-not-B task developed by pioneering developmental researcher Jean Piaget (Piaget, 1954). An infant is repeated shown a toy hidden in one location (labeled A), and when the toy is then hidden in a different location (B), they continue to reach back to A. The behavior is striking -- the infant just saw the toy being hidden, tracking the experimenter's movements with great attention (typically novel, interesting toys are used). And yet they appear to forget all about this in a flash, reverting back to the previously established "habitual" behavior.
The computational model we explore here (Munakata, 1998) shows how a range of behavioral phenomena, some of it quite subtle and complex, can be captured with a relatively simple model that shares much in common with the Stroop model explored above. Development in this model is operationalized simply as the strength of the reverberant excitatory connections among PFC neurons, which are the only mechanism for active maintenance in this simplified model. The "older" networks can hold onto information for a longer period of time due to their stronger recurrent connections, while information is much more fleeting in the "younger" ones with weaker recurrent connections.
To see how this all plays out, open the A Not B model and follow the directions from there.